How to Build & Sell AI Agents: Ultimate Beginner’s Guide

Two years ago, I taught myself how to

build AI agents without any prior

experience in AI. And since then, I've

started multiple AI businesses that have

generated over $5 million in revenue.

I've grown from 0 to 450,000 subscribers

here on YouTube. And I've built AI

agents for some of the biggest companies

in the world. It's pretty safe to say

that learning how to build AI agents has

completely changed my life. So, in this

full course, I'm going to teach you

everything, and I mean everything that I

have learned over the past 2 years about

building and more importantly making

money with AI agents, even if you don't

know how to code. My hope is that you

too can learn and use this incredibly

powerful skill in order to build the

life of your dreams before these AI

agents start taking our jobs. As you can

see, by the length of this video, I'm

not going to be holding anything back.

So, in order to make it a bit easier to

consume, we're going to be splitting it

into three different chapters. First

we'll build your foundational

understanding of AI agents, covering

what they are, how they work under the

hood, and the key concepts that you need

to know before you actually start

building them. And this is without any

technical background required. Secondly

we will be diving into four different

endto-end AI agent tutorials, taking you

over my shoulder every step of the way

as we build some of the most popular AI

agent use cases on the market today. And

I have carefully planned these builds

out to give you a taste of multiple

different noode platforms and different

AI agent types. and you'll learn how to

build each of these step by step

watching over my shoulder. And finally

I will give you my proven blueprint for

monetizing your AI agent building skills

over the coming years while this

technology continues to explode in

adoption and popularity. I'll be sharing

the exact strategies that I've used to

generate millions of dollars with this

skill set over the past 2 years. So

let's get into it. Now, if you're new to

the channel, let me quickly share why

I'm qualified to teach you about AI

agents. So, if you are new to the

channel and don't know who I am, my name

is Liam Mley and 2 years ago, I started

learning about AI with no prior

experience in the field. I was teaching

myself how to build AI agents and chat

bots all through my own self-study

which I documented right here on this

YouTube channel from day one. So, you

can go back and watch all my previous

videos of how I got from there to here.

This led me to starting Morningside AI

which is my AI automation agency where

we build AI systems and agents for

businesses from basic customer support

systems to full AI SAS platforms and

even we've built my own AI agent SAS

platform, Agent, which is now over

45,000 users. At Morningside, we've

worked with publicly traded companies

and even recently an MBA team. I also

run the world's largest AI business

community with over 120,000 members on

school as of this recording. and through

my community and through this YouTube

channel here, I've taught hundreds of

thousands of people from all backgrounds

really how to build and make money from

AI agents. So, everything that I'm about

to teach you today is exactly what

helped me to achieve all of this. So

let's dive

in. Now, if you look at how long this

video is, you'll realize that there is a

lot to cover here. And now, I don't want

you to give up halfway through the

video. So, let's quickly take a moment

just to get clear on why AI agents are

quite literally the next big thing. And

I know that sounds cliche and you hear

it all the time, but seriously, they

are. And why learning to build them is

by far one of the most valuable skills

that anyone can have over the coming

decades. And if if my experience over

the past 2 years is anything to go by

that should be enough proof for you guys

to believe me. So, stick with me. But

here's the hard truth about AI and jobs

right now. According to the latest

research, McKenzie predicts that AI and

agents could automate up to 50% of

current work by 2030. And the World

Economic Forum reports that 41% of

companies plan to reduce their workforce

due to AI. Now, a lot of this sounds

doom and gloom, and of course, many

people are naturally worried about their

career in future based off seeing this

kind of data. But it's not all bad if

you know where to look. And this video

isn't about making you feel all sad.

It's about uplifting you. And if you

look on the flip side of the same data

these same reports reveal an enormous

opportunity for those willing to seize

it. So the World Economic Forum's future

of job report states that 50% of

employees plan to reorient their

business in response to artificial

intelligence. And due to this

reorientation, 66% of employees plan to

hire talent with specific AI skills such

as prompt engineering. So on one hand

we have the expectation of massive

layoffs and automation of work over the

next 5 to 10 years. But on the other, we

have the majority of employers searching

for people who have these AI skills or

really just basic AI literacy. Why?

Because AI literate employees who can

automate parts of their work can have 5

to 10x the output of someone who doesn't

have any AI literacy. And I promise you

that brushing up on your AI and reaching

this point of AI literacy so that you

can be on the winning side of this next

5 to 10 years is actually so much easier

than you think. I mean, it's easy as

watching this entire video in order to

build your AI skills base to make a big

step towards AI literacy. And if you

don't believe me when I say that a

little bit of self-study like this video

goes a long way, here's a great clip

that I've seen recently on the All-In

podcast from one of the most respected

investors and technologists in the

world, Navar Raakan, alongside a whole

bunch of other very smart billionaires.

Again, I would say the easiest way to

see that AI is not taking jobs or

creating opportunities is go brush up on

your AI. Learn a little bit, watch a few

videos, use the AI, tinker with it, and

then go reapply for that job that

rejected you and watch how they pull you

in. And so this video is exactly what

Naval is talking about. So whether

you're an aspiring entrepreneur wanting

to learn valuable AI skills and launch

an AI business like I have, or you're a

business owner wanting to just

understand agents so you can use them to

grow your business, or maybe you're just

wanting to make sure that you are the

last person that your boss thinks of

firing because he or she's an AI wiz and

I can't afford to lose them. Then I have

made this video for you guys. Now, what

I want you to do is close out all of

your other tabs. Go get a notebook and a

pen and a beverage of choice and make

sure you make a commitment to yourself

right now in order to finish this

training and ensure that you are going

to be empowered by AI and not replaced

by it. Now, if you've done all that

let's get stuck into

it. All right, so step one in building

AI agents is knowing what the hell an

agent actually is. So, 2 years ago, when

I first started learning about AI

agents, I had no idea what they actually

were. The term AI agent gets thrown

around a lot in almost like everywhere

these days. You got AI agents this, AI

agents that. But what actually is an AI

agent? Well, the clearest definition

that I found that helps beginners to

really wrap their head around what they

are is this. An AI agent is a digital

worker that can understand instructions

and take actions in order to complete

tasks. So, in a very simple way, just

like businesses have employees who

handle different tasks, an AI agent is

like having a digital employee. But the

cool thing is that you can build them

and you can make them do whatever you

want. You're like literally building an

employee that you can put to work to do

things for you. And of course, they cost

much less to run than a human and they

don't need sick days and they don't

start beef with Mike over in the sales

department because of his comment at the

coffee machine. So, I'm sure you can see

the appeal of this kind of digital work

and AI agents to businesses who are

looking to adopt them.

In order to really understand why these

AI agents are such a big deal, we need

to look at where we are coming from. So

most of you have probably encountered

those chat bots on websites before. You

know those little little chat widgets

that pop up saying like, "Hey, how can I

help you?" So these kinds of chat bots

are pretty basic, right? They a lot of

the time they're useless and they're

they're kind of like a waiter who can

only really recite the menu but can't

actually take your order or or bring

your food. They can't do anything. They

just respond with some kind of

pre-written answers. Well, nowadays it's

a simple AI generated answer. But AI

agents are different, right? So, here's

an example. If you ask a regular chatbot

about booking an appointment, it might

say, "Oh, our business hours are 9 to5.

Please call to book." And that's it.

They just give you some information

back. But with an AI agent, it could

actually go and check the calendar, find

some available slots, go back and forth

with the person that they're chatting to

in order to book an appointment, send

you a confirmation email, then update

the business's scheduling system and CRM

automatically in seconds. This ability

to take action is what makes agents so

powerful. They're not just fancy chat

bots. They're actually digital workers

who can search through databases, update

spreadsheets, send emails, book

appointments, generate hold documents

and much much more. And so building and

deploying an AI agent is a bit like

hiring a new employee because when you

bring someone into a business, you need

to firstly explain their roles and the

responsibilities to them. You need to

give them access to your system so they

can use them. And you need to trust them

to handle those tasks independently. And

now when we are building agents, as we

see later, it's exactly the same, except

these agents are going to be working

24/7. They're never going to get tired.

They can be duplicated and modified

instantly. And they cost a fraction of

what a human employee does. And this is

exactly why understanding how to build

and sell AI agents is becoming such a

crucial and valuable skill these days.

Because whether you're an entrepreneur

looking to scale your business or you're

an employee wanting to become

irreplaceable and and make more money at

work, knowing how to create and deploy

these digital workers is like the

biggest cheat code in the whole world

right

now. Now that you understand what AI

agents actually are, let's look under

the hood and see how they actually work.

Just like humans need a brain, memory

and tools in order to do their job, AI

agents need specific components in order

to function correctly. An AI agent needs

five key parts in order to work.

Firstly, every AI agent needs a brain.

In the AI world, we call this a large

language model or an LLM for short. And

you've probably heard of some of these.

You've got GPT from OpenAI, Claude from

Anthropic, Gemini from Google, etc. You

can think of the LLM as having a super

smart intern who can understand your

instructions in plain English, and then

figure out how to get things done from

those instructions. So, without this

brain, all of the other parts would be

useless, right? It's like having a whole

desk full of office supplies but having

no one sitting there in order to use

them. Secondly, the brain needs

instructions on how to behave. And this

is prompting. So writing a prompt for an

agent is how you program a lot of the

behavior of it rather than having to

code it manually. And this is really

what makes building AI agents so much

more accessible to non-coders as the way

of actually programming the

functionality and how they work is done

through clearly written instructions

rather than having to actually code it.

Thirdly, agents need memory. Imagine

trying to have a conversation with

someone who forgets everything you said

30 seconds ago, right? So, memory is

really important because it allows your

agent to remember what you talked about

just a few messages ago, keep track of

the tasks that it's been working on

build on previous conversations, and

even in more advanced ones, it can learn

from your past interactions. And the

good news about memory is that most AI

agent platforms completely handle this

memory component automatically. So, you

don't need to worry too much about it.

But just know that it is an important

part of a functioning AI agent. The

fourth component of an agent, and this

one is optional, but it is external

knowledge. AI models like GPT and Gemini

are pre-trained on a huge amount of

data, but that data is basically cut off

at a certain point, eg 2024. It's kind

of like having a new employee who only

really knows what they learned in

school. But just like you can train an

employee like that with your company's

specific materials, you can also give an

AI agent additional knowledge on top of

the information it was trained on

through providing things like PDFs of

your company documents, spreadsheets

with product information, customer

service transcript, or basically any

other textbased information. Without

this added knowledge, agents will be

limited to general information and

couldn't handle specific business tasks.

But as I said, knowledge is optional and

you will only need it in some builds.

Finally, and this is the most important

part, we have tools. So tools are what

transform an AI agent from just being

able to chat to being able to actually

get things done. So you can think of

tools like giving your digital employee

access to different softwares. Just like

you might give a new hire access to your

email or your calendar or your CRM

system, you can give an AI agent access

to digital tools that let it take

actions when needed. These tools let

your agent do things like checking

real-time data, updating databases

sending messages and notifications

creating documents, all the stuff we

went over just before and much, much

more. The really powerful part, which

we're going to cover later, is when

agents use multiple tools together in

order to solve complex problems, just

like us humans would use multiple

different websites and softwares when

doing our tasks. Now, let me show you

how all of these parts work together in

a real example. So, say you want an

agent to handle customer support. When

the agent is sent a message, the brain

immediately understands the prompt that

it has been given and also understands

what the customer is asking. It checks

its recent memory before replying each

time to understand the full context of

their conversation. And if the brand

detects that the customer wants a

specific question answered from the

knowledge base, it will use its external

knowledge in order to deliver the right

information to them. And finally, it may

use tools to update a customer's account

or to process a refund whenever required

during the conversation. So all of these

things are happening in seconds as the

conversation is going on. Which is why

AI agents are such a game changer. They

can combine all of these components in

order to create a fully capable digital

worker that very very closely replicates

how humans

work. Now that you know the anatomy of

an agent and the five parts of it, a

more practical framework for

understanding how we actually plan and

build AI agents is what I call the three

ingredients. Basically, you only have

three elements to plan when creating an

AI agent, which when mixed in various

ways can create millions and different

types of agents for different use cases.

This is because the AI model or brain

can be easily swapped in and out and

isn't really a major factor in the

performance of the agent as any of the

top models that you pick from any of the

different providers at any given time

they're all pretty good. And also, the

recent chat memory is handled by default

in almost all cases when you're building

on these platforms that you're going to

see later. What this leaves us with is

what really matters when building and

planning AI agents. Firstly, the

knowledge, the external data that you

want the agent to be able to use when

answering. Secondly, the tools, the

different actions that you want the

agent to be able to take, eg saving the

contact info to the CRM or getting some

live data on stocks or sending an email.

And then finally, prompting, which is

the glue that ties everything together

and determines how the agent behaves.

So, write these down. While the agent

has five components, the brain, the

prompt, the memory, the knowledge, and

tools, your main focus as an AI agent

builder is in the three ingredients of

prompting, knowledge, and tools. In the

next chapter, we'll be looking at how

you actually build an agent using the

different combination of these three

ingredients. But first, we need to dive

deeper into the keystone of

understanding how to build your own

valuable digital workers. And it all

comes down to

tools. Now, we need to dig a lot deeper

on tools as they are by far the most

powerful part of AI. agents. But in

order to understand deeply and be able

to build powerful agents with them, we

need to take a few steps back and

actually cover the basics of how

software and the web and internet as a

whole works. Now, this is as techy as

it's going to get in this video, but I

promise once you understand this, it's

so important and it's literally like

having a superpower. So, please stick

with me through this. So, remember how

we said that tools are what allow agents

to take an action to actually do things

rather than just chat? Well, the way

agents use tools and do work online is

just how we do it as well, but with one

key difference. Instead of clicking

buttons and typing into forms, agents

use what we call APIs. And every time

you use the internet, you're actually

making dozens of requests to APIs as

well and getting responses back, but you

just don't realize it. So, let me show

you what I mean. So, when you click on

this video, here's what actually

happened. Firstly, your browser sent a

request to YouTube servers saying, "Hey

I want to watch this video." And then

YouTube servers sent back all of the

data needed. And thirdly, your browser

unpacked that data and started playing

the video on your screen. So this

request and response pattern happens

with almost everything that you do

online. When you open up Instagram, you

are requesting your feed from Instagram

service. When you send a tweet, you are

sending your data through Twitter's

service. And when you check your email

you are requesting from Google the

latest messages in your inbox and

they're sending it back and your browser

is loading it. Thankfully, we get pretty

websites and apps that make it very easy

for us to do this and use software via

APIs through a nice application. But

under the hood, it is still two

computers talking back and forth

requesting, sending, and displaying new

information for us on our screen. These

request and response happen through what

we call APIs, which are application

programming interfaces. So, you can

think of APIs like waiters in a

restaurant. Basically, they're going to

take your order or your request to the

kitchen, which are the servers of the

business, and then they bring back your

food, which is the response. So, you

have request and response, and you have

you as the client, and them as the

server. There are two main types of

requests that you can make. Firstly

either a get request. This is basically

just like asking for information like

checking the weather or looking up the

price or loading this video. You're

requesting to get the information to do

something. Secondly, we have post

requests, which is when you're sending

some kind of information like posting a

tweet, sending an email, or uploading a

photo. So, go back and write both those

down because we're going to be using

them extensively in the building section

of this video. Now, here's where it gets

interesting. So, AI agents use these

same APIs as their buttons to do things.

So, each tool an agent has access to use

is essentially an API that it is able to

call. So, these kinds of tools come in

two different flavors. We have pre-made

integrations like Google Calendar or

Gmail where it kind of comes out of the

box ready for you to use and just plug

straight into your agent. And then we

have custommade tools that we can build

ourselves. So you can think of pre-made

integrations like buying a readymade

meal where they've done a lot of the

hard work versus custom tools where we

are like cooking from scratch. And both

work, but custom tools give you a lot

more control. And this is a skill that

I'm going to be teaching you in the

second chapter of this

video. Okay. So now you got the basics.

Let's get clear on how a tool is

actually made and what the key parts are

as you're going to be using them a lot.

So, let's break this down using a simple

example of a text capitalization tool.

It takes in some text and the outputs

the capitalized version of it. So, first

to create a tool, we need a function. We

need something that does work. In this

case, it's super simple, right? It needs

to take in text and it needs to make it

uppercase. So, this can either be done

through a basic Python function or you

can use an LLM to do this as well.

Basically, we need to build some way to

capitalize the text that we give to this

function and actually do the do the

work. Next, in order for the AI agent to

use this function, we need to wrap it in

an API. So, we have the function and

then the API wraps around it. And this

is essentially making that functionality

we created accessible over the internet

via APIs. Without it, the function

cannot be used by our agent. And in

order to use the API that we've just

created and use this function inside it

the API is going to expect the same sort

of inputs that the function needs. So

the input of the text that we want to

capitalize and it's going to output the

capitalized version. So this is very

important to remember function. It takes

in the input of the uncized text does

work and outputs the capitalized

version. We're basically then just

building an API around it so that we can

put it on the internet and then we can

have an agent that knows how to call

that API can send information into the

input go through the function and then

get spit out and then our agent catches

it at the end. But the magic step and

what has really caused the AI revolution

to kick off is that we can explain to

our agent how to use this API just by

explaining how the API works in natural

language. And this is where schemas come

in. A schema is like a one-page

instruction manual on how to use an API

and therefore how to access the

functionality inside that API. And when

an AI agent is given one of these

schemas, it too can read that

instruction manual and determine things

like what the tool does, what

information it needs as an input, like

we talked about before, and what

information to expect as an output. Now

they may look scary, but they're

actually really, really easy to

understand, and we're going to cover

them in the next chapter with this

video. And the good part about it is

that these days, schemas are

automatically created by many of these

no code platforms that you build agents

on. But I'm teaching you this because it

still helps to know what they are doing

and what that what's really happening

under the hood on these platforms. And

there are still going to be times where

you may need to roll up your sleeves and

do it yourself. The incredible part

about these schemas is that modern AI

like chatpt can read these instructions

and perfectly understand not just how to

use it and like okay I need an input and

then I expect an output but also when to

use it. For example, let's say we had an

agent and we gave it that capitalization

tool that we just talked about and then

we said can you please capitalize this

text? Mary had a little lamb. The agent

would then read over the schemas that we

provided it and then it would see that

there's a tool with a description saying

this tool capitalizes text right in the

instructions for the capitalization

tool. We would have said this thing is

for capitalizing text and it takes in

some text and it gives you the

capitalized version. And so the agent

will read that and see okay this looks

like based off the instruction they just

gave this is the tool that they want to

use. And then it will check the

requirements and see that the tool takes

in one input in string format which is

just text which we have described as the

text to be capitalized. So it reads all

this. He says okay it it needs one

input. It's in string format. So I know

I need to give it some text and okay

what does this text do? It's the text

that they want to capitalize. Great. So

now it knows it needs the input and it

knows that this is where it's going to

send the text to be capitalized. Then

now that it knows what it wants, it goes

back to our message and it intelligently

extracts Mary had a little lamp. not

hey, can you please capitalize Mary had

a little lamb? It's smart enough to know

that we want that taken out. So, it will

take that part, Mary had a little lamb

out of our input, and then it sends that

to the API where our capitalization

function does its thing. Then the API

sends back the capitalized version plus

a bunch of other response data as well.

Then the agent looks at your original

question, looks at this messy response

it got back from the API, and then using

its brain, the LLM, it writes a natural

language response answering your

question. It would say, "Here's your

capitalized text colon Mary had a little

lamb in all caps." That may sound

complicated. It may have gone over your

head. Please go back and just listen to

it again. You really, really need to

understand this process of uh the

message comes in, looks at the schema

realizes, okay, it wants to use this

tool. Okay. What do I need to do in

order to use this tool? Okay. Well, then

I'm going to grab it out of the input.

I'm going to put it in here. And it can

actually go back and forth. Say our

capitalization tool needed some other

input. Say you needed to provide uh the

number of letters you wanted to be

capitalized. It may see that this tool

needs two inputs and I've only been

given one. So then it will go back and

ask me, hey, could you can you please

tell me how many letters you want to be

capitalized and you will see this magic

in the agents that we're going to build.

When the agent can ask you questions in

order to help fulfill the needs of the

tool, you have this very intelligent

system that really will blow you away

when you see it in action. And one thing

many people miss about this process is

the agent actually gets back raw

computer data from the API or what we

call JSON. But using the LLM, it can

transform that into natural conversation

and answer your question in a very very

uh clear and concise way. So it's

basically like having an employee who

can read all this technical information

and then explain it to you in plain

English, which is another part of why AI

agents are so powerful. And so when you

understand this pattern that we've just

gone through, I promise you, you will

never see the internet the same way

again. Every action online is just

requests and responses. And therefore

we can build our own tools and AI agents

to automate all of it. So instead of you

manually searching the web, copying

information, pasting it into

spreadsheets, sending emails, an AI

agent can do it all automatically using

tools if you build it correctly. It's

like having a digital employee who can

press all of these API buttons for you

thousands of times faster than any human

could. And don't worry if this feels a

little bit technical. In the next

chapter, uh I'm going to show you how to

create your own tools like this from

scratch using platforms like Relevance

AI, uh where you can build out powerful

tools without writing any code. and will

really start to click into place once

you see the stuff in action in the

building section. But before we get into

that, let me reveal the power of AI

agents which is unleashed when they are

given multiple tools to work

with. Now, obviously having an AI agent

that just capitalizes text isn't very

useful. I get that. The real magic

happens when you give agents multiple

tools and the ability to use them

together in order to achieve complex

goals. So, do you remember our

definition? AI agents are workers that

can understand instructions and take

actions to complete tasks. When you give

an AI agent a task, it's going to try

its best to execute on it, but if it

doesn't have the right tools on hand to

do the job, it's going to be useless.

And so, the more tools that you can give

an agent, the more flexibility it has to

solve problems just like a human would.

So, let me give you a real example from

my own business, right? Say I build an

agent and give it the task. Find AI

startups that have recently raised money

and put them in a spreadsheet and add a

summary of each of the businesses in the

spreadsheet and then email me the link

to the spreadsheet. When you give an AI

agent a task like this and provide it

with multiple tools to use, it can break

down this problem just like a human

would. For example, it might think first

I need to search for AI startups using

my web searching tool. Okay, let's do

that first. Then I'll need to create a

new spreadsheet with my Google Sheets

tool. And then for each company that I

find, I'll need to add a row to the

spreadsheet. And then I'll need to write

a summary of each business and put it in

a new column. And then finally, I'll use

my email tools in order to send the link

to Liam. And that's all great, but then

when you add on top of that powerful

reasoning models like OpenAI's 01 and 03

and even things like deepseat as the

brain of the agent that can plan, take

actions, then reflect and then plan

again and so on. You have essentially

created a truly intelligent AI that

solves problems and approaches them just

like a human would. So, say for example

the original plan was to use the web

search tool to search for AI startups

raising money. Probably a terrible

search term, but what if that doesn't

return any good results to the agent?

Well, a human would go, damn, I need to

change my search term or maybe I need to

try find a different method of finding

these companies on like LinkedIn or

something. The latest in AI technology

like these reasoning models, it allows

these agents to do this exact same kind

of reflection and replanning in order to

achieve their objective. And this is

when you can really see why we call them

digital workers because they can do

things like planning multiple steps.

They will use different tools in a

sequence and even adjust their approach

based on the results from those tools.

Now, I should mention that this

technology isn't perfect yet, right? So

these multi-step tasks are often

unreliable and agents typically need

human supervision for more complex

workflows. But things are moving

incredibly, incredibly fast. In fact

we're already seeing the next evolution

which is multiple agents working

together. Instead of just one agent

trying to do everything, you can have

one main agent that you give orders to

and then it can use all of the other

agents underneath it as tools where it

can send specific instructions. Like

underneath the main agent might be a

research agent, which is best at finding

companies and has its own tools. Then

you have a writing agent that's really

good at writing summaries. Then you have

an emailing agent, which has got all the

emailing tools. And so each of these

agents can be specialized in their

specific task with multiple tools. and

then they all work together to achieve a

common goal. This is exactly what major

companies like HubSpot and Microsoft and

Google are building towards. It's these

entire workforces of AI agents that can

handle complex business processes

automatically. In the next chapter, I'll

show you how to build AI agents like

this for yourself using no code tools.

But first, we need to understand the

different ways that these agents can

actually be used in the real

world. So, we understand how AI agents

and tools work under the hood. Now

great. If you don't, please go back and

take some notes, right? You should by

now have a whole bunch of notes um from

the stuff that we've covered already.

And this stuff that you're learning took

me two years in order to learn and and

be able to apply effectively. So, you

best believe it that it's going to take

you two to three watches before it all

sinks in. So, if you're feeling a bit

lost and and overwhelmed, don't worry.

That's how it feels with learning

anything new or how it should feel if

you're learning something that's

actually pushing your boundaries and

adding something to your to your

capabilities. Next, we need to look at

the different ways that AI agents can be

used in the real world. There are two

main categories of AI agents.

Conversational agents and automated

agents. Conversational agents are ones

that humans interact with directly

through chat on things like websites.

You've got maybe you're chatting to it

on WhatsApp. You've got interacting with

it over the phone via phone call. You've

got chatting to it via Instagram DMs or

custom apps and websites. For example

OpenAI's GPT platforms allows you to

create agents that you can chat with

directly on your computer or on your

phone. or using platforms like my own

Agent, you can connect these agents that

you build onto a WhatsApp number or onto

Instagram. And I'll show you how to do

this in the tutorial chapter of this

video. So, in all these cases, you or

someone else is there sending messages

or instructions to the agent and

explaining what you want to do and kind

of chatting back and forth with it

whether it's on a website, WhatsApp

Instagram, or whatever. And within these

conversational agents, it's not just

text based. It's like I said, there's AI

voice agents as well, which are an

extremely exciting sector of the AI

space right now. And these systems use

multimodal models that can take in audio

as input and then produce audio as an

output. And so these agents can be

chatted to over the phone or via audio

rather than via text. This AI voice

stuff is super cool. And in the tutorial

section, I'm going to show you how to

take the exact same AI agent that we can

chat to on a website and then connect it

to a phone number and talk to it on the

phone. But then we get to what I call

automated agents. And so these are

slightly different from the

conversational ones. The truth is that

AI agents don't always need humans to

talk to them and use them directly. All

they need is some kind of input or

instructions to trigger them and that

tells them what to do. This means that

we can build these automated agents that

instead of waiting for some kind of

human input, they are actually part of

larger systems and processes and they're

triggered automatically by events like a

new email received or a form submission

or they work on schedules like once a

day and they essentially work in the

background without necessarily having

human oversight or input. For example

later in the video, we are going to be

building an automated agent that is

triggered by a new form submission. When

the form is submitted, some of that form

data is taken and sent to the agent

which then causes it to use the tools

that we've equipped it with and follows

the instructions in the prompt that we

gave it in order to make decisions and

take appropriate actions on our behalf

in a fully automated way. We are still

sending the message to the agent, but

it's not a human needing to type it

manually or speak it over the phone.

There's no human step. The input is

being automated in some way. And this of

course opens up a huge number of use

cases for AI agents in businesses

especially. And of course I'll be

showing you how to build both types of

these conversational and automated

agents in the tutorial section of this

video. But the last step of building

your foundation of knowledge before we

move into that is to look at some real

world examples of how businesses are

using these AI agents right

now. So firstly we have the personal

assistant category. And this is what

most people think of when they hear the

word agent. something that you can chat

to that's going to update your calendar

and sort of send emails and even make

phone calls for you. Um, now these are

all nice to have features, but honestly

uh this space is likely going to be

dominated by the big tech giants. You've

got OpenAI through Chatbt trying to do

this with Tasks, Google through their

suite of apps and connecting them to

Gemini and Apple through Siri. These

guys are going to eat up this entire

market of personal assistance and your

own personal AI agent that helps you do

personal stuff. the real opportunity

lies in business applications and how

people like you and I can build and sell

AI agents to businesses which we're

going to be covering in depth in the

final chapter of this video. So, we've

got the next chapter which is going to

be on building the four tutorials and

the final chapter is all about how to

sell and how to monetize your AI agent

skills that you've just learned. One of

the core use cases for businesses right

now are what's called co-pilots. And

these are AI agents made for specific

roles in a business. We're going to be

building one of these later in the

video. And these specialized AI agents

are essentially helping someone in a

specific role in a business to do their

job more effectively. Take a customer

support co-pilot for example. It would

have a knowledge base that allows reps

to get answers to customer queries

instantly and deliver them over the

phone. So they've got the little

co-pilot up on the side there. They're

on the phone. They get a question, they

can search and for an answer in the

knowledge base, it gives them back and

they can give it to them over the phone.

This same agent could also have a tool

that allows them to look up the customer

information very quickly. Um, I could

have another tool that it makes it very

easy to send a summary of the call into

the database so that the next rep who

picks up the phone and talks with them

knows exactly what was discussed

previously. It's like giving every

support rep some kind of AI assistant

that makes them dramatically more

effective. It also makes their customer

support a lot more consistent as to what

the company wants people to be saying

which is a a big problem with managing

large customer support systems. And then

we have lead generation and appointment

setting agents. These are probably the

most valuable type right now. And

businesses are using these on their

websites, through WhatsApp, on

Instagram, and even over the phone to

engage and have conversations with the

interested people who are approaching

the business 24/7. They can offer

instant answers about products and

services. And they're even smart enough

to be able to capture emails and phone

numbers mid-con conversation for later

follow-up by sales team. Some can even

book appointments on the spot and mid-

conversation by using a tool to check

the calendar availability and then using

another tool to create a new booking

once they've agreed on a time with the

prospect. Another real world agent use

case and one of my favorites is a

research agent. And so these can help

businesses by automatically researching

leads that come in through their website

or elsewhere. And when someone fills out

a form, the agent can spring into action

and start searching the web for

information on the company, finding

their LinkedIn profile of the person

they're going to get on a call with and

gathering any other valuable data that

it can find. Then it can take all of

this information and generate a summary

of who this person is and what this

company is also and decide whether

they're a good fit for working with the

company and if so then they can send the

sales team some kind of detailed brief

or suggested strategy on how to close

this particular person on a call based

on the research. So it's basically like

having a an automated team of

researchers who as soon as leads show

interest in your business, they're out

there figuring out everything about them

and determining one whether they're a

good fit for you and your products and

services which is called qualification.

Then secondly, if they are qualified

giving the sales rep something that will

bring them up to speed on who this

person or who this company is and how

they can try to close

them. So, we have covered a lot so far.

So, before we dive into each of these

agent builds that I'm going to walk you

through over my shoulder, please make

sure that you've got your notes taken

out and the core concepts of this video

so far understood properly. You should

be clear on things like what is the

definition of an AI agent? What are the

five parts of an agent? How is building

an AI agent like being a chef? And how

many ingredients do you have to play

with? What are the two main parts of a

tool? And what do schemers do? So, pause

the video now and try to answer these

questions. And if you aren't 100%

confident, you need to go back and watch

it again. So, don't rush this or you're

going to feel way out of depth when we

get into the tutorials that we're going

to be covering next. But if you are

congratulations. You are one step closer

to AI literacy and becoming a much more

valuable uh participant in this global

economy. So before we get into the

second chapter, there's just three very

quick things from me. Firstly, if you

are a business owner who wants to fast

track to becoming an AI leader within

your industry, at my agency, Morningside

AI, we offer everything from AI

education and upskilling programs for

executives and staff to AI strategy and

roadmap consulting and of course AI

development services as well. So we

would love to help you get ahead. So

feel free to get in touch via our

website in the description below. And

secondly, at Morningside, we are hiring

for all sorts of roles right now. So

whether you want to build AI systems for

some of the world's biggest companies

that we have as clients or to help

produce videos like these that are seen

by millions of people or create

educational material for thousands of

businesses. Uh we have roles for all

sorts of things right now. So you can

apply using the link in the description.

And please, even if you're just vaguely

interested, I really recommend you just

check out the link and see what roles

we're hiring for. Uh you never know

what's going to be on there. Um and it

may be a very good way for you to use

your skills to fast track into the AI

space by working under myself and my

team. And finally, if you have gotten

any value so far in this video, please

head down below and leave a like on the

video. It helps me reach more people.

Um, I put a lot of work into these

videos and it also lets me know that you

enjoy this kind of content and that I

should make more of it. And of course

if you like this kind of content and

want to see more of it, you can

subscribe so that YouTube will put my

videos up for you whenever a new one is

released. So, there's also a little

share button if you want to click that.

That'll let YouTube know this is good

content and that you're sharing it to

other people. Not only will that help

me, but you can share it to your friends

and family who may also or you may want

to help them to brush up on these skills

or help them give a way to get on the

front foot with AI. And that's what I

really make these videos for. So, thank

you for sitting through that little bit

of housekeeping and self-promotion. Now

let's get stuck into the

building. I have carefully assembled

this chapter on building to give you the

most bang for your buck possible in

order to kick off your AI agent learning

journey. We are going to be covering

four different use cases across four

different AI agent building platforms.

These are all no code, so don't worry

about that. And the chances of you

falling in love with at least one of

these platforms is pretty much 100% as

you're going to rapidly start to connect

the dots uh about how you can start to

use these kinds of agents and these

platforms in your own life or in your

work or for your friends and family and

those around you. So, here's a quick

rundown of the builds we're going to be

getting into. The first build is going

to be a sales co-pilot built with

relevance AI. And here we're going to be

building three custom research tools

from scratch, including an advanced web

scraping tool, which is a a great skill

that I want to teach you. And with

these, we are going to be creating a

conversational agent to help the sales

reps at Big Boy Recruits, a hypothetical

fantasy uh recruitment firm, in order

for them to be better prepared for sales

course. So that's the purpose of the

sales co-pilot. The second build is

going to be an automated lead

qualification agent. And this will be

built on a platform called N8N. And this

time we will be helping Big Boy

Recruits, our fantasy recruitment firm

to automatically research and qualify

new leads and then send an email

notification to the correct sales rep.

And this is going to show you that

automated style of agent where it's

built into a process rather than having

a human input necessarily. In build

number three, we will be building a

website and phone-based lead generation

customer support agent. This will be

built on voice flow and it's going to be

able to do three things. Firstly, answer

questions from a knowledge base

generate instant quotes using a custom

tool we build and also do lead capture

on interested prospects. We're then

going to slap this agent onto a website

widget so that you can chat to it via a

website and via chat and text. And then

we're going to take that exact same

agent and connect them to a phone line

so that we can call our agent over the

phone and access all of the same

functionality we just talked about. And

finally, for build number four, we'll be

using my own AI agent platform, Agent

to rapidly build a lead generation agent

and connect it to a WhatsApp number that

we can chat to. The leads that we

collect are going to be automatically

sent into an Air Table database for

later review. And please don't skip

around these builds as they're all kind

of connected in some way where we're

reusing parts from build one and build

two, etc. But without further ado, let's

get into building some agents. All

right, people. Enough of the theory. Uh

now we get into the fun bit of actually

building these agents out. So, I've done

a lot of work and my team has done a lot

of work. So, thank you to the my team

members who have helped me put this

together. Um, putting together four

different AI agent builds for you. And

this is really going to walk you through

an A to Z all the different platforms

that you really need to care about, all

the different kind of core use cases and

functionality. There's a lot more of

course, but this is going to really give

you the foundation that you need to

succeed in the space. And hopefully

it'll be the thing that kind of sparks

your interest in it because I I want you

guys to have fun with it. these big

tutorials for me. Honestly, when I put a

lot of work into it, I build up the sort

of mental resistance to it because I

know how much work there is going into

it and I have to make this big whole

session where I'm all uptight about it.

But I'm just going to try and relax and

enjoy this. And I really want you all to

do the same. So, set a bit of time

aside. You can either pause this video

put on your watch later, but I really

want you to take your time with this.

I'm going to be doing this more. So

when you do tutorials like this, there's

a few different ways you can do it. I

can either do all the building and then

give you the templates and kind of just

spoon feed it to you. And that's more so

what you do for someone if you're trying

to like really fast track them and they

don't want to learn all the skills, but

um I I know what I'm trying to build

here for you guys. And I'm going to give

you a sort of stream of consciousness.

You just get to see me kind of jamming

out and building these things. And I'll

be explaining my thought process and the

concepts etc along the way to reinforce

what we've learned before. So I'm just

going to dive into it with our first

agent.

And so what I've done is put together a

big Figma board here which is going to

be breaking down all these different

builds. So under here there's some

goodies you see. Oh, there's some

goodies under each of these that I've

put together. Um and we're going to go

through them one by one. Starting off

with agent one over here. I mean there's

a lot of stuff here um that you guys are

going to get. So you'll get the whole

Figma and it includes all the templates.

So if you do want to just kind of watch

through this, pick it up. You can either

do it and follow it step by step with me

and see how I build it and really build

those flexible skills that you're going

to need to succeed in the space or you

can just watch it and be like, "Okay, I

kind of get what he's doing and then

take all the templates from me at the

end." That's I mean, completely up to

you. Depends if you want to be a really

really nerdy builder about it and get

into the weed like like I like to do. Um

or you just want to be like, "Hey, I

want to do this my business. I want to

roughly understand how these things work

and what platforms." So, use this

resource as you will. But we're going to

jump into agent build number one here

which is our sales co-pilot built with

relevance AI. So, running through this

quickly, we have the purpose of this.

This is basically going to look a bit

like this. It's going to be a co-pilot

and co-pilots work in that you have a uh

it's basically a specific AI agent that

you build for a specific staff or staff

member or role. So, say this case, it's

going to be a sales co-pilot. It'll be

the thing that the sales rep uses to uh

in their day-to-day as they're working

on their jobs. You can add tools like in

this case, you see we're going to have

three different tools here for our

agent. One's going to be a company

researcher tool. So this is when the

sales rep would be like, hey, I have a

call coming up soon. Um, let's put in

this I need to research this company cuz

this is who I'm going to be on a call

with. So they'll put in the company URL.

This tool that we're going to create is

going to go and research that company.

It's going to bring back and give a

summary. And then it's like, okay, well

this is the LinkedIn URL of the person

that we're going to be got on a call

with shortly. It's going to pass in the

LinkedIn URL. It's going to take that

URL. It's going to pull all the

information and write a summary about

the person. So now we have the company

summary and we have the person summary.

And the final step here is going to be

what I'm calling a pre-all report

generator. And that's going to take both

that company and prospect research that

we've done. It's going to combine them

together and be for this specific

company. As you're going to see that

this hypothetical company we're building

this sales co-pilot for, it's going to

generate a basically a pre-core report

or a strategy uh a strategy prep for the

sales rep so that they go onto those

calls much more prepared and also sort

of a personalized guide on how to try to

close this person. So, um, all of these

templates are going to be here. Each of

these are templates for the tools. And

this is for the agent as well. Um, but

here's some more information. You guys

can pick through this as you wish. But

um, this is the kind of end result and

we're going to be able to chat to it.

And this would be something you could

build for a client. You could build it

for your own business or you could just

tinker around. You could build co-pilots

like this on relevance for yourself. So

that's why I want to start with

relevance because it also is a platform

that we can build these tools on. So

it's a really, really good one to start

with and let's get into it. So, the

first step of course is to go to

relevance AI. So, I'll put a little link

up here. You guys will be able to get

this Figma. It'll be on the school. Um

all of the information and all the

resources for this are going to be like

this Figma is going to be linked to the

school. My free school community. If you

haven't already joined, biggest AI

community on school, biggest AI business

community probably in the whole world.

Um, so we can jump across that first

link in the description. You'll be able

to find this in the YouTube resources

section. Um, pretty straightforward. Of

course, when you click on this, it's

going to ask you to log into relevance.

So, if you haven't already, you can make

an account. Um, it's fairly low cost.

They have a free plan, then a team's

plan, I believe. Um, so it's not too

much, but it is a really, really

valuable tool as you're going to see.

So, you can sign in here. I'm going to

jump in with my Google account. There

may be a bit of setup for your account

but I'm sure you guys are smart enough

to figure out how to set up an account.

I'm sure relevance also makes it easy

enough. So, then we get taken to this

dashboard, but we see on this left hand

side, we have tools. So these are the

tools that I've talked about um where

it's some kind of functionality that we

can create and we can build it all on

relevance no code and there's even sort

of extensibility or you can add more

functionality in to relevance by adding

some low code components or even custom

code. So relevance is a really great

base for building not only tools but

then the agents that you can connect

that into and we're going to use the

same relevance tools that we make now in

multiple of the different agents that

I'm going to make for the rest of this

video. So first things first if we go

back to the Figma here we see we need to

make three different tools. So tool one

is company researcher. It's going to

take in a company URL. It's going to

search the web and it's going to return

a summary. So, that's the functionality

we need. Let's go and create a new tool.

Going to call this um

research company. We can give it a cool

little I'm going to zoom this up for you

guys. Hopefully, that's the right size.

Um uh search. Have fun with this stuff

guys. Like, if if it's putting stupid

emojis on things to to enjoy it, then

then that's what you need to do. Like

uh if you make it a chore, it's going to

feel like a chore, right? Um, now we get

to descriptions. Um, this is something

we're going to see recurring, but

basically as as you know, as we learned

in the concept section, we need to have

natural language descriptions of our

tools and of our APIs so that the agents

can read those descriptions and

understand uh what the agent or what the

tool does and what those different parts

does. So, you'll see this recurring

throughout this. But first things first

what does this research company tool do?

Um, takes and oh, I got caps lock on.

takes um and there's also some tutorials

here if you want to go deeper. Relevance

has some great documentation as well. Um

but takes in a

company

penny URL

and scrapes the website then returns a

sum and AI generated summary. So then to

build a tool we need to have some kind

of input. You don't always need an

input. It can actually just be

triggering, but generally you're going

to have some kind of input that the

agent needs to pass into the tool. In

this case, to research company, it's

going to need to take in some text

which is going to be a URL. Um, we're

going to say comp company

URL. And then again, here we have

another description. You see, describe

how to fill this input. This is again

going to help our agent within relevance

AI and elsewhere as you'll see in

tutorial number four. Why this is so

important to add in the descriptions

right? So this is a

URL for a company to be

researched must be in the format https

colon slash

um dot dot dot dot dot. So we need to

have the https for this to work. So

that's going to be our input. Now if

this seems a bit confusing just stick

with me. It will make sense in a second.

this stuff. If there's anything that

I've learned from picking up so many

different tools, like when I first got

into Facebook ads, when I've got into

building these kinds of agents, it's you

feel completely overwhelmed, but that's

all just part of the process. And what

feels difficult now is not going to feel

difficult forever. So, just please stick

with me. Um, and it's a really, really

great feeling once you go and be like

this was hard a few weeks ago and now

it's really easy. So, we've got our

first input. That's what the research

company tool is going to do. And then we

need to define our steps. So, the next

step, we can go add. I'm going to hide

this so we get a bit more space. Add

step. Now, the cool thing about

relevance is that it comes with a lot of

great functionality out of the box.

Here's one, extract website content, we

have LLMs, we have Google searching, we

have all sorts of AI generations

replicate um knowledge bases as well.

There's so much cool stuff on here and

this is why I really rate relevance as

one of the best platforms. If you were

to go all in and want to upskill, you

can build so much on this. Um, so I'm a

big fan. I love the the relevance team

and what they're doing. So the initial

plan for this build I was going to use

the extract website content which is

fairly straightforward. We can say oh

one other thing um the company URL we

are going to be using this company URL

throughout our tool here. So we can

change this text to say something more

descriptive. So company

u URL. You guys are going to got going

to think like think and write like

coders now cuz you need to uh use some

kind of syntax and use some kind of

variable naming convention. This is a a

standard one or you can do things like

company URL camel case but I prefer this

format as I'm I'm mainly a Python kind

of guy myself. Now that we have that

named we can use it in these kinds of

fields. So here you can see pops up use

inputs. So basically when this tool is

run it's going to take the inputs or the

information we put in the inputs and

it's going to pass it to different steps

and use them as we describe within the

within the builder here. So let's run

this quickly. Let's put in my company

https/

morningings.ai. Um, and then we can

click uh run here. So, that's going to

go to my website and scrape the

information off of it. There we go. So

it's got all of this, but you see it's

just pulling back the first page. Um

and this is why I actually I shuffled

this around. And I want to show you guys

how to do something a bit more advanced.

I know this is supposed to be a beginner

tutorial, but this is not really that

useful and I it's a very easy thing for

me to just bump this up to a little bit

more valuable. Um, while relevance tool

here is great, we can do better. So

we're actually going to delete this. Um

you can use that step for all sorts of

other things, but I really like what's

called fire crawl

firecroll web scraper. This is a cool

app. Um, firecrawl.dev. Shout out to the

guys at firecraw. Basically, if we then

go HTT Oh, I should just see if they can

do it. Morningside

doti and now do a free scrape for us

here. So, this is just going to do the

single URL just like we got in

relevance. But the difference here is if

we then go to crawl, if you hover over

this, it's going to crawl a URL and all

of its accessible subpages outputting

the content from each page. So, instead

of just taking that front page, it's

actually going to crawl through multiple

things. So this is really the first cool

thing or or first skill that I want to

put in your tool belt is that you have

things like fire call that you can use

their relevance. They have things like

map which is just going to output all

the URLs that it finds. Then there's

other things here where you can use AI

to extract data. I'm not going to go

into that. But what we want is this

crawling functionality from firecraw. So

I'll put a link in on the school post

that comes with this video. It again

first link in the description to go to

the school and if you go to the YouTube

resources section um there will be a uh

a whole post on this and all the

resources will be in there. It'll also

be in my free course on school as well.

So you can find it in the classroom

section. So you want to sign up to FCL

so you can get API key. It's very easy.

We can just go through with Google.

Again, this is not sponsored and there

is zero sponsoring going on through any

of these tools. I guess I'm kind of

sponsoring my own tool because I'm

putting it at the end. But I'm not

getting paid a dime for any of this. I'm

really just trying to put you guys on

what I like to use, what's made me

money, what's made me a more valuable AI

automation expert or developer for my

companies and for the companies you work

with. continue and then you get to the

dashboard here. It might look a bit

scary, but what you can do is go to the

API keys. So, you can click on create an

API key here

YouTube. I'm having issues with mine. It

should be fine for you. I've already got

an API key, but once you get the API

key, what you can do is take it and come

back here to

relevance. And you can go into the side

panel here. Where is it? Settings. And

then we have our API keys. So, we're

going to need to add more into this

later. So, keep an eye on this. This is

something you need to be familiar with.

Um, and you can scroll down to firecrawl

and you just pop it into this firecall

API key section here and you're good to

go. And you can come back. Oh no, we

don't want to duplicate

that. Now we've got our firewall set up.

We need to make a couple things. We need

to do a couple tweaks here. We have, of

course, the if you want to get those

variables up, you can go bracket bracket

um or curly bracket curly bracket, which

is shift um to get those. I don't know

why it's not popping up. There we go.

Company URL. And if we hover over this

we can see it says scrape the provided

URL only. Uncheck if we want to crawl

instead. So if we want to get that crawl

functionality that we just saw that we

think we want to get all the data, we

can uh uncheck

it. And then we want to extract the main

content. So you might have to just trust

me on that one that we don't want to

have all of the other rubbish. We just

want the body of the website. Um a

number of pages. We don't want this to

take too long. You can expand this much

more. Um but I'm just going to go for

say five for now just to keep it uh keep

it quick.

And then now what we can do is run this

again. We've still got my URL up here.

We can go run

step. Give it a second. You will at some

point have to pay firecrol. Of course

it's not a free service, but they do

have a free plan, so you should have no

issues with getting that. So here you

can see we're getting a lot more data

back from this web scrape than we were

with just the relevance version, which

is great. So the next step is we have

this data. We want to generate some kind

of research summary um so that we can

send that to our sales script once they

have requested it. So now it gets into

the fun part of writing LLM prompts. For

this one, sometimes you need to really

go and and make a big effort, which we

are going to do later as you'll see. But

in this case, we just want a quick

summary. I'm going to throw one in here

that I made earlier. All of this will be

available on the Figma or it'll be given

somewhere in the resources, right? But

um if we just need something quick and

dirty here, it's not really a massive

part of the project, so it's okay to

just whack one in there. So bang, I've

got it in there. Can you please take

this website content and summarize it

into a 300word natural language summary

which clearly outlines rad where they're

based, their values, etc. anything that

would be helpful to know for a sales rep

who will soon be on a call with them. Uh

break it into key areas like overview

products and services team etc. And I've

got a couple things here to make sure

that it doesn't mess up which I it was

doing for me a bit in testing. So we do

want to put in if we go curly bracket

curly bracket we want to put in the fire

call data here which is going to be uh

all the website data that came back from

our scrape. We want to then insert it

into this prompt here. So I hope you're

starting to the things in your your cogs

and your brain are starting to click

into gear here. And then we get to

select the model for this. It's pretty

basic task. So, I want something quick

and cheap. Um, for many of you, it's

going to be easiest to work with the

Open AI APIs because you've probably

already played around with your API key

before, which I'll show you how to do in

a second. But, let's go with GPT40 Mini

but Relevance, of course, does have

support for all of uh the other models.

But my tendency for most tasks now is to

actually go for some of the Google

models that have come out. Again, you

guys might be watching this in a year or

whatever. It might be quite different

but at the moment, Google is really

leading the way with making the cheapest

models possible, and they're actually

really good as well. the the price

decrease on using things like uh Google

Gemini Flash Light and Google Gemini

Flash 2.0 and stuff like that. It's

ridiculously cheap and it's also a

really good model. So, it's a bit more

difficult to get APIs on the Google

side. So, I'm just going to stick with

OpenAI for this tutorial. Um, so let's

go GPT4 mini. And then we need to of

course set up our API key. Um, if you

scroll down, where is the OpenAI? So, to

get your OpenAI API key, you need to go

platform.openai.com.playground/playgroundplayground

sorry. Um, this will again be linked in

the resources or you can just type up

platform.openai.com. I'm sure you'll

find it. If you haven't already, create

an account um, and sign in. And then you

need to go to your dashboard here. You

go on the left side, you go to API keys

and you click create a new secret key.

And then you're going to be able to copy

that key and bring it back into uh

relevance. Paste it in here. And then

you're good to go and start using the

OpenAI suite of models. It's pretty

easy, right? So now we have our

information back from the scrape. We

have our prompt in here. And then we can

go run step and see what this company

research tool is going to output for us.

Boom. Morningside AI is a leading

artificial intelligence development

company dedicated to empowering

businesses autonomous AI agent

development, enterprise consulting

chatbot development team. Uh they

got keep me out of my own team page.

Damn. But uh yeah, so there you go.

That's the that's the company research

tool. That's step one. Um I hope you

guys can kind of see how that works. Now

a cool thing about relevance is there is

this build section which we've just gone

through. But you can also go to use and

this can be really helpful when sharing

these kinds of tools. So not this is not

really only just an AI agent tutorial.

I'm also teaching you how to build tools

because you can build very valuable

tools and something like relevance. And

then you can go share. You can go

publicly

available. Oh, and I can click this and

then I can give to my employees. I can

give to the companies that I'm working

with, the clients that I've sold these

kind of services on. And then they get a

nice and handy tool like this. I mean, I

use these throughout my organization for

like description generation, a lot of

content repurposing. There's tons and

tons of different use cases for building

these kinds of tools. And then you can

take this URL and you can share it

around to whoever you want. So, this is

a uh a great way to use relevance. Let's

go back to our tool here. But for now

we need to keep moving along so we can

get this this first agent done. So

that's the first tool. I'm going to run

through it a bit quicker now that we

know how these kind of tool buildings

work. I'm going to create a new tool

here. I'm going to call it this time if

we go back to our Figma. And I recommend

when you guys are building your agents

you're building systems and planning

them out. This kind of laying out in a

Figma, if you're not familiar, this is

Figma. It's like a design software, but

you can also use it for kind of

whiteboarding. It's called a Fig Jam

board. It's one of the types of uh

boards you can do. And I use this all

the time with my team as well. It's like

if you can't take what I'm telling you

lay it out on a board so that I can

review it and give you notes and and

then we all agree this is the build. Um

there's often a lot of uh communication

issues with explaining functionality. So

this AI agent layout, it's really

helpful. You can if it's maybe a

workflow automation, you can do box box

arrow arrow arrow all of this laying out

how it's going to be built. Here I've

done it for you in a very basic format.

So we've done this first one. Maybe I

just make this green. Um second one is

going to be prospect researcher. So this

takes in a LinkedIn URL, searches the

web, and returns a summary. And you're

going to see how I tie this all into an

agent

shortly. So we're going to go research

prospect. Sil takes in a linked in URL

scrapes the profile and then generates

an of the prospect input. We're going to

need a link and URL the link linked and

URL of the prospect.

and we rename

this. Now, we're going to add a step and

relevance has got us here linked

um get a LinkedIn profile or company

post. So, this is cool cuz then we can

pop in our LinkedIn URL

here. Oh, we don't need two of

them. So, we're going to get the user

profile here. Then if I put in

mine my LinkedIn profile, if you guys

want to connect with me on LinkedIn

more than welcome to do so. I'll put in

the description below. Um, we can do a

little run step here. So, if we go back

over to data here, that's great. We've

got my about section. So, this goes very

long way across because it's in uh it's

in JSON here. It's got my company. It's

got my company domain, where I'm from

years, company, founded, tons and tons

of great information that you guys can

use and we are going to use shortly. So

this is really cool. I would probably

add one more step to this if I was

taking this um and building it for for

my own team. I would add in another

LinkedIn scrape here um where we just do

the same thing, but we also get the

posts because posts can give you a bit

more up to date um information on what

they've been doing recently. that you

can guys can add that and you just go

add a step LinkedIn and you do the same

thing as we've done here but you change

this to LinkedIn post get user post so

that may be a cool thing for you guys to

add the uh functionality on at the end

is a bit of a challenge you can pause

this video and do that and then we need

a llm step to take this again I'm going

to grab a pre-written prompt that I did

just to save some time going to drop

this in here says fairly similar stuff

um LinkedIn data I'm going to put all

the data in

there. And then we're going to use a

GPT4 mini again. And we can give that a

run because we've already got this data

queued up here. And there we go. We've

got a nice summary. Um, if I change this

to nice and formatted. So there's a

little button down there between raw or

formatted. And you can copy the stuff

out of here, of course. So Liam Mley, my

followers. Damn, I got a lot more than I

thought. So there we have the summary

my name, where I'm based, uh

information, my career experience. Super

handy stuff. And this is going to be

super helpful in the next step when we

generate that pre-core report. So very

quickly, we've created one more tool.

I'm going to save this. So now we've

got, if we go back to our Figma, we have

two of these done. Now the final one is

going to take in the company and

prospect research and generate a

pre-call report. So this one's going to

be a little bit different. If I go

create a new

tool, preall report

tool. Okay, free call takes in company

and prospect summary and generates a

free call report for sales

direct. And now for the inputs for this

we need long text, not just normal text

because we're going to be taking in that

big company and uh and prospect

research. So we go prospect summary

summary of the prospect based on length

and profile

that and a prospect in this case is

someone who's a potential customer. Just

to clarify that if you're new to

business and don't really get these

terms. Company summary, uh

summary, right? We have our prospect

summary and company summary in there. I

hope you're following along. Next step

is just an LLM step and we want to

combine these two together. Again, I've

got a little handy prompt for this to

save us time. Now, in this case, you

will see that the prompt is a bit

bigger, right? So this is um for more

important parts when you are creating

tools whether it's it's for agents or

just generally when you're using prompt

engineering and LLMs to create value. In

this case we are creating value because

in this case we're taking in this

prospect summary and this company

summary. We're also giving it the the

context of this fantasy or or

hypothetical business that we are

selling this agent to as like a co-pilot

system. We've got Big Boy Recruits which

is Dallas based recruitment firm

specializing in software industry talent

acquisition for SMBs. You're going to

see this kind of recur across the

different projects we do, but basically

we're helping these big boy recruits to

automate their business with AI. So

this takes in some context on that

business and it's going to generate a a

report that's going to help the sales

rep say, "Okay, this is the company.

This is what we sell. This is what we

specialize in. This is the company that

we're trying to sell to. This is who

we're going to be talking to. What's

some how can I personalize this call or

what's the strategy I can go into this

with? What are some angles that I can

attack this call from?" And so to do

this, I have a prompt writing tool that

I use quite regularly and my team uses

it as well. Um, perfect

prompt. So, this tool does a lot of leg

work for myself and my team all the

time. I'm going to give it to you guys

to use for free. You'll be able to clone

it into your relevance account.

Basically, what I'll usually do is I'll

put on uh the dictation thing. You've

got it on one of these keyboards. You've

got this little thing. basically

whatever on your computer allows you to

speak into the computer and it takes in

your voice and transcribes it into into

text on the screen. I'll press that and

then I'll explain as you can see here

what is this prompt doing and why and

I'll go this prompt needs to do this

this this is going to take in this

information it's going to do this the

reason we're doing this is this this and

I'll do like a big big body of text in

there and then the next one if I have

them I'll give some good examples of

input and output pairs of how I want it

to take in data and how I want it to

spit it out. If you give it both of

these and you hit run, it's going to

print you out using the researchbacked

prompting techniques that we use at

Morningside. It's all crammed in here.

There's a video that I recommend all of

you watch. It's going to be in the free

course anyway on school. So, when you

get in there and watch my prompt

engineering guide, um, this is basically

the entire information of that prompt

engineering guide smashed into this LLM

step here. So, when you pass this

information in, it applies all of that

and it gives you out a prompt that is

fully researched back and performs very

very well right out of the box. So

that's a little bit of extra value I

wanted to throw in there for you guys.

this is going to be available on the

school um with the rest of the resources

as well. So basically I put in the

information here about what this

particular uh task was. I said it's

going to take in the prospect

information. It's going to take in the

prospect summary and the company

information and it spat out this

basically first go and I just had to

insert these variables. So this prompt

and everything else will be on the on

the score resources as well. So in this

case because we are doing a bit of

strategy and sort of high level thinking

rather than just summarizing and we may

want to change the model here to

something a bit smarter. We could go to

03 mini which is one of the later ones.

Um, again, when you're watching this, it

might be 06 or 010 or whatever the hell

they come up with next, but there's

probably going to be some much better

models. So, just use a smart one because

it's really strategizing on how big boy

recruits can position themselves for

this call. So, enough of me yapping

about that. Let me grab some inputs for

this and we can test

[Music]

it. Shout out muscle. All right. So

I've got this information here about

myself, my LinkedIn profile, and my

company. And so again, remember that

this is for Big Boy Recruits, a Dallas

recruitment firm specialized in software

industry talent. So it's going to look

at my company, Morningside AI. It's

going to look at me and my background

and my profile on LinkedIn. Then it's

going to spin, as we see here, um

review this, analyze this, map big boys

unique value proposition, ra, and it's

going to try and create a report that's

going to allow the sales rep to sell me

or close me better on there or find some

angles to sell to at least. So if we go

run tool, and now you see that I'm using

a lot of just basic web scraping and LLM

steps. I just want to show you guys the

basics. The thing is tools can get very

very advanced when you have like CRM you

want to integrate into, but relevance

allows you to do all of that. It's just

within the scope of tutorial, it can be

pretty difficult to be pulling

information from all over the here cuz I

have to set up a database, show you guys

how to do it, too. So, this this keeps

it quite confined, but it still gives

you a good taste of it. So, if we look

at this view, all key business

challenges and opportunity, Morningside

AI, this Liam's profile gives me a bit

of a rundown of this mapping big boy

recruits unique value proposition. Maybe

it's going to be better if I change this

to the format. There we go. Talks about

mapping big boys recruits, strategic

talking points. I've been following your

journey of digital marketing, AI, ra um

dive into opportunity. I work at big

boys. This assist, and it's even gone

and done a section on anticipated

objections. So, the idea is that the

sales rep is going to have a skimmer of

this before the call, which ties into

the value that I've listed here, which

I'm going to do for all of these builds

by the way, which comes down to

ultimately a better prepared sales rep

should close more deals, right? if they

know more about the prospect and the

company and you have an angle to try

sell through or suggestions at least. It

should increase the conversion rate of

the sales team. So, we've built this

tool. We can change this to green now.

And the final step is going to be

heading over to our agent builder within

relevance. I'm going to save this. If

you pop over in the left panel here, you

can go into our agents. And what we want

to do is create a new

agent. We're going to call it our sales

co-pilot.

Um big

boy big boy sales co-pilot. Sorry, I

got, like I said, I got to have fun with

the stuff where I go kind of insane. Um

this this agent is

our sales co-pilot that helps reps to be

better prepared for sales

[Music]

calls. Triggers, we don't need to do any

of that. We go to core instructions. I'm

going to again paste in some of the

stuff that I've prepped earlier. So, if

I paste this in here, you'll see that

it's structured fairly similarly to the

prompt that we just did before for the

uh pre-core report generator. And this

is again using another tool that I've

created um for AI agent prompting. Um

so, it's fairly similar stuff that I I

include in that other prompting tool

but the agents is slightly different um

to just regular LLM steps within

different tools and workflow

automations. So, I will include this as

well. It's my AI agent perfect prompt

generator and it's fairly

straightforward to use. I'll include

that in there as well. But basically

you put in all the information about

what the agent is, why it's doing it

the different tools that you're

connecting to it, and then it prints out

this for you. So, I'll just run through

it. We've got a role here telling it who

it is, um, and kind of hyping it up and

saying how good it is. Explains the

task, um, talking about how it's helping

to conduct detailed research on

companies and prospects. Um, some

specifics. Uh, don't need to worry about

those too much. Just reiterating the

task. And now here we can enter in the

references to tools. So if we go slash

tool and see in order to get the agent

to function as well as possible, we need

to tell it what tools it has available.

This is really key across all the agents

you build, especially if they're more

conversational. You need to explain to

them what tools they have and how and

when they should use them. So if I go

you have three powerful tools. Research

company. Um, yep, that's right. Purpose

input, company URL, use when needing to

gather company information. This one of

course is the uh

prospect. And then this last one is our

[Music]

pre. There we go. So that's all whacked

in there. Don't need to worry about that

too much. But again, this will be

included um this prompt and everything

if you want to follow along and also if

you just want to clone the whole agent

um and use it in your own business or

sell it or whatever you want to do. We

also get to select the model here. Um

I'm just going to keep it as GPT4 mini.

I like some pretty fast responses here

because agents as someone's using it, it

can feel really irritating if it's not

responding quickly. So, we've got all

that built out. That's the core

instructions in the prompt of the agent.

Um, we can go down to the uh tools

section. It's got all the tools

connected in here because we mentioned

them in the prompt. And we can just go

through and do some quick settings on

here. Um, I don't want to have to do an

approval for it. Some tools you can say

look, they've got to give it a thumbs up

before it can actually uh trigger it.

Um, prompt for how to use. Just some

quick descriptions we can pop in here.

I'm not sure why relevance doesn't carry

that over from the tool. I guess they're

asking us to do it again for some

reason. Um when you need to research a

uh

prospect linked and URL, we're going to

say this is auto run as well. Um and

then preall change this to auto run as

well.

Use this when you need to generate a pre

call report from the company and

prospect

research. All righty. Um, and there's

all sorts of other cool stuff.

Relevance, as you can see, is like

abilities, sub aents, metadata, extra

stuff that you can build onto. Um, but I

just want to get you guys started with

the core of this. Um, all right. So, we

can confirm

that. And boom, we have our agent here

ready to go. So this is where you can

test your agents and use them if you

want to. But in this case, I'm just

going to give it a quick rundown and say

see if the functionality is working as

we as we planned. Um, hi, I am getting

on a call with Liam Otley from morning

side

AI. Here uh has

details or we can just go

[Music]

lenol report please to prep for the

call. Sending your task to big boy sales

copilot. And then we get to see all the

debug and how it's actually walking

through these different steps. Oh, let's

see what he

does. Yep. Okay, that's great. It's

using the research company as we wanted

it

to. Should add in one more step there to

research the prospect as

well. There we go. Using the second

tool. It is pretty satisfying when this

stuff works. And this is just a really

basic one, guys. I don't want you to

think this is like, oh, well, that's

pretty underwhelming, Liam. I'm trying

to teach you the basics so that you can

actually build on top of this. So if you

get the bug, if you get the like you get

a travel bug, if you get the agent bug

and you see the stuff and you're really

interested. Oh, look, there it is. Now

it's filling out the prospect summary as

the inputs. Surely we don't have to

watch it do that. It's going to take a

while.

[Music]

Um, I really write that out word for

word like that. But when you start to

see this magic and you add in other cool

tools and functionality, you test it on

yourself. You can build like things for

maybe you want to do content, you make

yourself a little content co-pilot

etc. There we go. To use all the tools

and it should be spitting back and bam

there we go. So, I hope that was worth

the wait. Let's go through it now.

Here's your comprehensive precore report

for your upcoming conversation with Liam

Mley from Morningside AI. Pre-core

report. Talent acquisition under

pressure. Morningside AI operates in a

highly competitive AI and tech market as

they scale. Finding specialized talent

engineers, data scientists, AI with a

proven record can be challenging. That's

scarily accurate because that is

literally one of the biggest constraints

that we have had to scaling Morningside

long-term is that it's just really

really hard even with my channel. It's

so hard to find the right people and get

them to commit as as developers as well.

So if you want to build a very big

general AI development firm, an AI

automation agency, you need the best

talent and you need to get a lot of it

in um so that you can scale up. So

that's bloody spot on. Obviously this

thing knows that's a good angle to sell

through. Um but yeah, prospect analysis.

Liam's a dynamic entrepreneur and

thought

leader with a robust background in

e-commerce, digital marketing and AI.

His journey reflects a passion for

innovation and commitment to continuous

learning. Again, that's pretty bloody

spot on um hands-on experience. So there

you go. That is the big boy sales

co-pilot for big boy recruits. The cool

thing you can do now once you have built

this um is you can go share um there's a

chat UI which I'm going to turn on now.

There are chat widgets so you can put

them on websites and stuff like that.

What I want to do is just pull this up

because this is what you'd be giving to

your client likely or if you guys are

going to start selling these to

businesses which again we're touching on

selling in the last section of this

video. So how do you turn these into

into a business and start making money

from it and selling these as a as a

service and building these businesses

which is really where the money's money

is made. So there you go. This URL you

can obviously send to your client. If

you're building it for your own team

you can send this to your team and say

"Hey, pin this because you're going to

be able to use it. Add more

functionality into it, etc. That is how

you build a co-pilot on relevance AI.

All righty, that is build number one out

of the way and we are jumping into AI

agent build number two, which if you're

listening closely at the start of the

section, we are talking about an

NATbased inbound lead qualification

agent that's going to be doing some

pretty cool stuff for us, which is a

really important function within a

business um around lead qualification.

Um, so this is a really cool one. Again

in this case, this is what we call an

automated agent, not a conversational

one. What we just built is a

conversational agent. humans are

directly talking to it and chatting back

and forth and using it and we are

operating it ourselves. In this case, as

you can see on this little flow uh flow

diagram here, this is a screenshot from

the final product. We are actually

baking this into a workflow that's going

to be triggered on a form submission.

We're going to do some research and then

we're going to use the handy AI agent

and tools agent within NA10 to trigger

another workflow and then send some

emails off. So, this capability of using

AI agents in workflow automation really

expands the possibilities of what you

can build. And the software NAN that I'm

going to teach you how to use is really

at the the cutting edge and leading the

charge when it comes to these automated

uh AI agent workflows. And just quickly

it's good that we walk through the

purpose and the value behind this

automation so that we know why we're

doing it. Right? So this inbound lead

qualification use case is based on the

fact that companies who market

themselves well soon have far too many

people reaching out to them. Many of

which are not a good fit or what you'd

call qualified for what they sell. Eg

they're too small or they're not the

right industry. Like you you have a

business and they say we only help XYZ

kinds of businesses. And if leads come

to that business who are not qualified

then they obviously don't want to be

taking calls or or doing anything

further with them. So this process of

researching a new lead and deciding

whether or not to take a call is known

as qualification, which is what this

agent aims to automate. So the value

here is that instead of having to pay

someone to manually qualify and go

through all of these leads or using

arbitrary rules, which is what some

businesses have to go to, it's like

look, oh look, we've got so many leads.

Let's just say if they don't want if

they say that they're not on this, then

we'll just cut them out. And that's

potentially leaving money on the table

by cutting out leads who would have

actually been a good deal, but the rules

kind of didn't see enough detail to be

able to determine if they're a good fit

or not. So, this automation is

essentially immediately qualifying and

triggering the next steps to the sales

team and allowing them to do that human

style research on these leads at scale.

So, enough talking. Here's a little bit

more information. Again, with all of

these, it's going to be on the figure on

the school. Then, I've also broken down

how this agent would actually operate in

the real world and sort of real time. A

lead's going to be submitting the form

which is going to be this here. um the

relevance company AI researcher. So

something that we've just built in

relevance, we're going to reuse in here

which is handy that you can start to

move these components around and see how

they can fit into different automation

platforms. Um then the AI agent is going

to look at this information from the

research. Then it's going to determine

based on a qualification criteria we

give them um inside the prompt of this

agent whether they are qualified or not.

If they are qualified, it's going to uh

use this tool here and call this second

workflow. when we're going to

essentially analyze that further and do

a notification to either our agency team

or our our SAS team and if they are not

qualified we will use this tool here

which will just send an email straight

back to the person who submitted the

form say hey sorry we're not open to

working with businesses like yourself at

the moment let us know if we can help

you any other way so that's a rough

rundown of the build let's jump into it

so to kick things off of course you need

a platform on natn.io

io. All links and resources will of

course be on the school uh post for this

video. And once you're on this page, you

can go to get started and you can create

an account for free and just go through

the setup process that they do. I'm sure

you can figure that out. They do have a

14-day free trial as of this filming. So

that's very good if you're just jumping

in, not having to pay anything. And they

give you quite a lot of usage up here.

As you can see, 1,000 executions. So

what we're going to do, of course, is

click on create workflow up here. I will

be giving you the template. So if you

want to just import it, you can. That

will be on the Figma there. But just

like the last tutorial, I'm actually

going to be showing you the process of

building these up from scratch that you

can see how I how you go through the

process of building these automations

and the the testing and back and forth

you need to do in order to get to the

end result. That's probably actually a

lot more important if you are to go out

if I'm trying to teach you to fish, not

just give you a fish, is to see how I

deal with problems when we're building

these. So, let's get started by starting

off our trigger. We're going to go

form has a nice

form on new inn form event. And here we

get to create an N8N form. So we can

call it uh work with

us. Provide

your. So now we get to pick the field

names. So we want to have the first one

which is what

is make that a required field. We add

another one. What is your company

website?

DG HTTP this morning. Require

field. Right. So, we've just built out a

basic form here with first name, company

website, which we're going to need for

the next step. I've put a placeholder in

here so they know that it needs to have

https um at the front of it. And I'm

asking for them to provide some

information about your inquiry like and

maybe you can say what can we help

you? Um and that's going to be text

area. So, they've got a bit more room.

So, we can go test step here. Make sure

that it's all looking nice. This is what

the form's going to look like. What's

your name?

Obby. There we go. We've submitted that

form. If you go back, there we go. We

have the data in. So, this shows you the

output. NAM works by having kind of this

middle island here, which is what you

set up. And then the left side is the

input and the right side is the output.

So, we've tested it and these are the

outputs that our form is giving, which

is what we are looking for. And the next

step in this lead qualification process

is to do some research on the lead. So

we have the company URL and this is when

we're going to go and make an HTTP

request. So this is basically calling

any API over the internet. And in order

to set this up, we actually need to go

back to

relevance and we're going to find the

company researcher tool that we made

with relevance. And now you're going to

start to see how this all fits together.

um that building tools and relevance can

also be very useful and an extremely

useful skill in all areas of AI

automation because now I can come on to

research company and not only can I use

it here this I mean this is why I think

relevance is such a great platform um

you have the use here so I can send this

across to a client I can share it with

it as I showed before I can use it just

here myself I can run it in bulk on a

spreadsheet or but more importantly in

this case I can go to the API and now I

can call this might look scary just

don't worry I can call this

functionality basically send in a

company URL and get back the research. I

can access this over the internet

through an API and they give me it here

and they tell me exactly how to call it.

So now we this is on actually a post

request. So we can copy this. Remember

how we talked about get and post

request. This is a post request because

we're posting some data to relevant AI.

So we change this method to post. We put

the URL in here. We do not need

authentication in this case but you can

turn it on. So you can make a private

here and then you can have an

authenticated. might sound a bit

complicated but for now don't worry

about it we don't need to have an

authentication step on

this and then if we look at the request

body here it tells us how we can send

data to relevant AI and if we go copy

here come back we're going to send a

body it's going to be in

JSON and we can change this to using

JSON and then we can paste in basically

what we have been given from relevant AI

now this looks a messy. Let's pop this

open a bit

more. And we actually need to change it

to an expression here. So fixed means

that we're not accepting any dynamic

data in. So when the form is submitted

we actually need to take in some data

from that form which we have over here.

We need to inject it into this uh HTTP

request to relevant AI to get this

company research done. So we can't have

it as a fixed um JSON body here. Need to

change it to expression and then we can

pop this out here and it gets a bit

easier. So, we have params. We have the

company URL. And then we need to pop in

here. Oh, the company

URL. Pop that in there. And here on the

right side, we get to see what that

would look like given the test data that

we've just put through. So, you can see

I've got the company URL, https

morningside.ai in quotations here, which

is what we want. So, we can go back now.

And now we can give it a test to see if

it's going to be able to communicate

with relevant AI and get us the data we

want. There we go. We have our result

back from relevant AI which is the

summary. As you can see when we go back

to this um and back to build this is

exactly what we'd expect. You know you

put in the URL does this scrape a fire

call writes the summary spits out the

summary and this summary is what we're

getting back out over here which is what

we want. So bank that's great another

step

done. And those of you who are a bit

confused about what this is this is just

the body of the request. So because we

are sending a post request remember how

we have get and post request. Get

requests are typically just with a URL

and with a bunch of stuff tacked on. A

post request, we need to send a a JSON

body like this. And it does look quite

confusing, but if I take this and go

JSON

form, paste this in and beautify it. And

you can see how we have basically the

project which is the relevant project

that we are calling. And this tells the

API this is the project that we want to

interact with. and the project expects

the params, the inputs, which is the

company URL, and we're injecting that

company URL from our information here in

NA10 that we've dragged across. It might

seem tricky a few times, but trust me

this stuff becomes like riding a bike

once you get up and running. So, um, a

few more of these and you'll be you'll

be completely fine. All right. And so

now that we have our company

information, the next step is going to

be setting up the agent, which is really

the coolest part in my opinion about

make right now is that we come in here

and we can click on um agent and we can

set it to as a tools agent, which means

we connect our own tools. And if we just

back out of

this, we can do cool things like set up

the model. And right here, this is so

cool because we get to see exactly what

we were just talking about earlier in

this video, but we have the different

parts of an agent, the different

ingredients, right? So here's the chat

model. This is the brain. This is the

LLM that's going to be powering the

whole agent using the soup example. Like

this is the one meat that we get to

choose, right? This is a specific model.

In this case, we going to be using

OpenAI again because you already have

your API key and I can't really be

bothered going and showing you a whole

another provider, but it's the same

process for all of them. You can if you

want anthropic, you can then pick all

the anthropic models. Uh but in this

case, just to keep it simple, let's just

go back

to and open AI model.

And if we go back again, you can see

that we now have uh the memory and the

tools that we can connect. So, of

course, we have the tools, which we've

talked about a lot in this video

already. We can connect multiple

different tools here, as we're going to

do in a second. And then we also have

memory set up here, which is a little

bit outside of the scope of this video.

As I said, in most platforms, it comes

builtin, but NAT is a bit more of a

developercentric platform. So, if you

wanted to play around with memory, um

different forms of of managing memory

you can do that here. In our case, we're

not going to be touching it. And in

order to connect a knowledge base, if we

wanted to set up a knowledge base for

our agent, we would connect it as a

tool. So you can see here we've got

in-memory vector store, pine cone vector

stores, etc. These are vector databases

that we can connect just like in the

other tutorials we're going to do in

this video. When you upload some

documents to make a knowledge base

they're essentially being put in a

vector store like these, but the

platforms manage it for you and make it

a lot easier to do. So in this case

we're just going to be doing two

different tools. Firstly, we're going to

be calling an NATM workflow. So I'm just

going to finish off the the basic setup

of this. Um, and then we're going to add

another tool on. It's going to be the

Gmail

tool. And then before you know it, we

have our AI agent structure built out.

So, we're using the Open AI models.

We're going to pick the model shortly.

We're going to be using the tool to call

the second NATM workflow, which is going

to be uh triggering the the email

notifications for our sales reps and the

classification of the uh of the lead.

That's going to make a bit more sense

when we actually do it. So, just stick

with me on this. And then this Gmail is

going to be sending back a hey sorry you

didn't qualify for what we do um sorry

we can't help you let us know if we can

do anything else for so to start setting

things up we can start from left to

right here with the chat model um the

openai model that we want to use and you

need to set up your openai account so

you can click create new credential and

you need to go and add in your API key

here I'd suggest you go and make a new

one on platform openai.com and you can

add a new one in here and you can name

the key nat so you start to know which

keys are used for which different

platforms um and once you put that in

there you can Just click save and then

it will run a little test and then

you're ready to go. You should have this

set up here. Then we get to select the

model that we want to use for our agent

here. And in this case, I want to go for

something quite smart. So, I'm going to

go for

03. We have 03 mini here. This one

appears to be a little bit more recent.

So, they'll sometimes put the dates on

the end of it. And if you just look at

the current date, you can kind of see

how how close to the current date it is.

Um, but I'd say this one's a bit more

recent, so it's going to be hopefully a

bit better. And next, we're going to

skip this cuz we need to set up a whole

different workflow to connect it to. Um

we're going to jump straight into this

Gmail one. You need to set up again

another connection as you go through all

of these different automation platforms.

You do need to do these these

connections between uh your own account

say your Gmail or your calendar or all

these different apps. You need to go and

create a new credential. Um and you can

just do the sign in with Google here.

Super easy to do. I'm sure you don't

need my help with that. Once you've set

up that connection, you can close this

and you will see the connection that you

set up here. Basically, that's what

we're going to be sending emails

through. And then we can get into

setting this up. So, because this Gmail

tool is going to be used to send a a

reply back to the person who submitted

the form and say, "Hey, sorry, you're

not a good fit for us." You want to send

it back to the person who submitted the

form. Now, if you scroll down here, uh

yep, you see that I've I've forgotten to

add the email form in. So, this is a

good example of needing to go back a

little bit. So, we can go back to the

form submission here. Um, scroll down

say, what is your

email? And we can set it as an email.

So, it's going to automatically force

them to provide a valid email for us.

And I want to maybe shimmy this up a

bit.

Um, so it's name, email, then company

website. Um, we can do another test here

just to give it some proper

data. Oh, it's not going to let us do

that because this isn't set up properly.

So, we can just delete that for

now. And we're actually going to delete

that. Otherwise, it's going to be a bit

of a pain.

So, we want to test it

again. So, we can submit another form

here and go back to NA10. And there we

go. We have the information. We have the

email now. That's great. And so, now we

can come and set up our tools again.

We've got the workflow there and we've

got the

Gmail. Um, and now we have our

connection set up. Yep.

Oh, and we haven't got the data here

because we need to run this

again and get the research. So, now the

research is done in there for us to set

up the Gmail tool here. We can go to two

and we'll be able to pull in the email.

So, again, they submit this form. We

realize that they're not a qualified uh

person for our offer. And then we're

going to send an email back and say

"Hey, sorry, you're not a good fit." So

we can say the subject here is um thanks

for your interest. I'm going to change

the email type to text here and I'm

going to write a basic message in. I'm

just going to snag it from the one that

I've done previously. So, we need to

change this from fix to an expression

because we want to be pulling in their

name here. So, I'm going to paste in

what I have here. Again, this would be

included um in the resources. It's just

going to save us time if I don't have to

type this all out manually. Um but you

can see here, I'll just delete this so

you guys know what we're doing. If I go

hi, or hi, and then we can add in what

is your first name? Hi, name. In this

case, it's going to be filling in my

name here as an example. So, highly mly

thank you for your interest in big boy

recruitment services as you specialize

in recruitment for software and

development agencies. We're not a good

fit based on your company's industry.

Please let us know if you'd like to

connect us with one of your partners who

specializes in dealing with your needs.

Cheers. Huge Jackman here to sales, big

boy recruits, BBR, uh, Dallas, Texas.

So, if you guys remember huge Jackman

comment down below um for the OG fans.

And then that is our Gmail all set up.

And just quickly so that you've got a

bit more knowledge around how this Gmail

uh tool works, we have all these

different steps that we can use. We're

using the send one. So it's sending an

email. You can use reply, you can use

get, delete, all these other functions

but the easiest one and the most common

one you're going to use is going to be

that send one, of course. Now, we need

to set up this NATM workflow which the

agent is going to call as a tool um when

they are a qualified prospect. So I'm

going to delete this one and just save

this for now. Then we're going to go

back

to home. We're going to create a new

workflow.

And this is a really cool skill that I

want to teach you. The fact that you can

build all these workflows and then

connect them to agents and it can just

be taking data in and kind of shooting

it off in all directions and triggering

all these complex multi-step processes

because it's a super valuable way of

using agents. Um, so I really want to

teach you that. And obviously this one's

going to be starting off a bit different

to the other one. We actually want this

to be set up as when executed by another

workflow. So that's going to be what the

trigger is here. And we are going to be

able to define using fields below. Let's

just add in one here that is a lead lead

name. Um, for now we can just leave that

there. But that's all that we need to

set up. We need to go back over to the

other one. Just needed to set this part

up. Let's rename this qualified lead

lead classifier and

notifier. So we can save that there. And

we have a bit more work to do on this

other one. If we go back to this, we can

rename this here. So let's call this our

lead

qualification agent.

And so now we have our other workflow

set up. We can come here. We can call

another workflow with the tool and we

can call it lead is qualified. And so

now is when we get to tie back into what

we learned in the foundation section

because we are now writing descriptions

for our tools. Remember how we had

schemas and scheas are basically

written instructions or instruction

manuals on how to use tools and how to

use the APIs that wrap around them. Um

this description is going to be

basically those descriptions that you

put in the schema. But NAT is going to

be basically constructing it for us in

the back end. And we just get to put in

here, okay, what's the name of this

tool? It's going to be called lead is

qualified. It's giving us a nice example

of how we can write a description for

the tool. So call this tool to get a

random color. The input should be a

string with a comma separated names of

colors to execute. So in our case, we

can say call this tool when the lead is

qualified according to our criteria. The

inputs should be lead name, lead email

company, company summary, and request in

info. We're basically telling that AI

model or the brain what this tool can

do. So when we send it some kind of

input, it then it looks over our tools.

It looks at the Gmail description and it

looks at this uh workflow tool

description and goes, "Oh, well, I have

a tool that does this and a tool that

does this. What have they just sent me?

Okay, now I think I know what I need to

do from here." So this is the rough gist

of what we want to do as a description

for this tool. I'm actually going to

beef it up with a bit of a a bigger one

here. Um if the lead is qualified to

work with big boy recruits, eg they are

software based business like SAS or

development agencies and trigger this

tool and send the lead data in the

following format. It's just dummy data.

So name a name email um an email message

I want new div qualified true company

information and company information

which is a summary of the relevance tool

to do the company research that we have.

So, might seem a bit crazy at the

moment, but stick with me because it

will make sense in a second. We're

basically just told it when it's going

to trigger this tool and the format to

send the data in. And then for the

workflow, we get to choose here the one

that we just set up, which is our

qualified lead classifier and notifier

at least in my case. And then we see the

workflow inputs that we've just set up.

So, if we go back over to our other

automation, so when we open this up and

define our inputs here, you can see over

here we are getting just one of them

that we've put in as an example so far.

So now we need to set up all these

inputs correctly. And we have the name

that we want. So we've got the name and

we want the email. So we can add another

email. What else do we need? We need the

message.

Um, honestly don't think we need this

qualified one here. And then we have the

company information.

So, if we just test this, head back over

here and

we refresh this

list. Oh, back out. Save

it. And this pops up. It says that these

inputs are outdated. So, there we go. We

have lead name, email, message, etc. And

then we can actually automatically fill

out a lot of these inputs. Maybe I will

put this back in here just to show you

guys how these work. So if we go um

qualified. Um and then we just call it

uh true

here. I just put it as a string.

And we go back here and we add in one

more which is a

qual

qualified which is also a string. And so

we have this qualified field here as

well. If we go back um I'll just test

this. Save it

again. Come back over and update

this. I think we can actually make it

even cooler. So let's go. Um it changes

from a string to a boolean. So that's

either true or false. Um, and if we test

this, save it again, and we change this

to take away these little things. Sorry

pisses me off if I don't have this set

up right. Um, and then we update this

you'll see this turns into a switch. So

that's a true or false, right? In order

to trigger this tool, it should be

qualified by default. So, it's a little

bit redundant, but it's still cool to

show you how we can get AI to fill out

these fields. Um, in a lot of cases, we

don't, but for this qualified run, we

can. So, we can set this as an AI

generated field. We can say if the elite

is qualified based on our

criteria this set to

true. And then for the rest of

them we can just fill this out

quickly. Give this a

second. Open this back

up. And we can fill out some of these

fields. So we can go lead name that we

want to pass through to the other

automation is going to be that. So we

can fill a lot of these in email

um message and the company information

um we can get from um this technically

but um maybe we could do a cool AI

generated one here which is um a short

summary of the

company and the industry that they are

in.

company's details. So, this is basically

telling the AI model of our agent how to

fill this field out, which is one of the

reasons that AI agents are so powerful.

So, that's all set up. Now, we've got

all of our tools set up. The last step

is just to set up a prompt for our

agent. And I am going to cheat a little

bit here and just throw one in that I've

done before to save us a bit of time

here. So, we want to set the prompt

here. We can paste in this information

here. And this is basically just telling

the agent who it is and what it's

supposed to do. You're a lead

qualification agent. Your job is to

analyze the form submission and company

research provided and then decide

whether they are qualified to work with

big boy recruits. Ra we specialize in

XYZ. Um we are specialist in capturing

talent for ra. We only work with

softwarebased businesses, EG SAS

companies or development agencies. These

companies are willing to pay much more

developers than your average marketing

company or local business. Therefore, we

only work with them. Your job is to

determine if the lead you are provided

with is a good fit for big boy recruits.

And if so, call the lead is qualified

tool and send the elite information to

it. If lead is not qualified, then you

must trigger the Gmail send email tool

for us to respond to them letting know

letting them know we are unable to work

with them. And then we have a response

format here which we can probably just

delete. And then we can add in here is

the lead

to information for you to

analyze. Let's pop this out to make it a

bit easier.

We can add in um just go name um company

URL

in go message

request and of course we go a

company and we take it. So that's from

the relevant step for the research that

we did to scrape using our firecrawl

tool. And then we provided all the

information to this agent and it's going

to be injected with all of these values

on each form submission and then it's

going to make a call on what tool it

needs to use. So we're pretty much

there. We can even give it a run here

and try to test the step and see which

tool it's going to try to

choose. If we go back we can see okay

look it's used the chat model as the

brain and it's triggered the NA10

workflow as expected. You can see here

that it's sent off information to our

other workflow. It sent the lead name

the email, the message, and the company

information, and it set it as qualified

as well. So, all of these fields have

been filled out. We've got a nice AI

generated summary here from the model

and brain. And we have the qualified set

to true. And so, the final step now for

us is to head back over to our other

automation and just finish it

off. Oh, we need to save that. I will

just run that again for you so you can

see it in slow motion. It's using the

LLM as the brain and the tools agent and

it's deciding whether it's qualified or

not. And if it is qualified, then it

will send it to this workflow. Bam

we've sent it. And there you go. If we

head back over to um let's save

this. Head back over to our qualified

lead classifier notifier. Now, we can

add on a quick few steps here. I'm just

sort of going to rip through this. Um

it's not super important. Um but it just

shows you a little bit more

functionality of what you can build in

on N10. So, we're going to add in a

messenger model step here, and we're

going to choose uh 40

mini. And what I want this to do is to

take in that information that we sent to

the workflow about the company research

etc. So, we know this is a qualified

lead now, but we just want to split it

between either our SAS team or our

development agency sales team. So

they're specialized in dealing with

different cases. So, I'm going to cheat

and just throw in a prompt here, which

you guys will be able to get access to.

Um, which is basically saying we have a

new inbound lead. Um, change this to an

expression. Sorry. Um, we have a new

inbound lead that we need you to

classify into either SAS or development

agency. Here's the lead information. Um

we need to go back step and test

this. There we go. We should have some

information. Um, and now we can put

these in. So, you see how there was

nothing here before I went back and

tested the trigger so that it gives us

some null values here that we can fill

out. Here's lead information. Um lead

name uh message

um

name

request company

information if the company is a SAS

output SAS if the lead has development

agency upput agency. So we're looking

for just agency or SAS as the outputs

here. Um simplify the output. Yep

that's all good. So we can no point in

us testing that step there because all

the values are null. Um but the next

step is a basic

router flow

if so this is a basic conditional

routing. So we have the conditions we

can go expression here. So we can go

um the content here. So this is the

output from the open AI step. If the

content which is the response from the

LLM step the classifier it's either

going to be agency or it's going to be

SAS. So if it let's just to to make it a

bit more flexible. If we go string, if

it contains

agency, great. So, if it contains agency

on the true side, we want to go

Gmail and we want to send a message. Um

and then if it is false, we want to do

basically the same thing. Now, I've got

a preset email just to help us out here.

Um, okay. So, here we're not getting

much data on the input side here and we

can't seem to simulate it because it's

of course triggered by another workflow.

What we can do is just save it here. go

into

executions. And if we go back to our

other workflow, the

agent, and we can go to the form

submissions

one. If we go to executions, and we just

run one of these that we just did

before, copy to

editor. And we just run this

again. This is basically just going to

trigger this again. and so that we get a

fresh execution and we can sort of pull

that data back into the workflow. Oh

we're having issues

now. It's cuz I need to delete this.

So if

we stop that and we just run it

again. Boom. Triggered it. And that's

all done. Now if we go back to this and

we go to executions, go to the most

recent one that succeeded. It's going to

load in. Oh, hang on. This one's

probably it.

Oh no, that's not

it. Okay, so this one here, if we click

this, yep, we've got all the data in

here. So, what we can do is copy this

into the editor and then we've got the

data that we need that's already loaded

in so that we have some values to put

into our Gmail steps Gmail. So, that's a

handy little trick to to know how to do.

And now we have all of this information.

So, that's what that's what I was trying

to get. Um, the same setup and we're

going to send this to I'm just going to

use an example here and call this um

it's the same email. You wouldn't pull

this in necessarily. I'm just using this

as something that I can show you

at show. Say new agency lead. Let's do a

text. We say um new agency lead man. Go

get

him. Um turn into an

expression. And then we say we can just

throw this company data in there. It's

going to be messy. You can play around

with this more when it comes to

formatting, but just to show you the

functionality. If we go uh test a step

here, that's going to sent an email to

this. This is like my agency sales reps

uh email. Of

course, um let's rename

this. Can duplicate this. Right click

duplicate, bring it

here.

Oh, connect this up. And we change this.

You change this to your like SAS guy.

You change it to a different different

email. Um, of course, and then you can

say new SAS

lead. Right. So now we have done all of

that basically all built out. The data

is going to come in from the agent. It's

going to send in the company summary.

This is going to classify it into being

a uh agency lead or a SAS lead because

those are the only two types of

businesses that we work with. So all of

them will be qualified when they come

through here. And then it just sends an

email to our uh agency sales rep or our

uh SAS oh rename this to our SAS sales

rep, new SAS lead for them to continue

and do the next steps and follow up

with. Right. So to test this we can turn

this on to active and you can see that

you can now make calls from your

production form URL. Um we can go okay.

If we double click on this we can open

this up. We can click on production URL.

Copy this and open this up in a new tab.

And now we can give it a

spin. So of course my agency Morning

Side AI does development services. So

this should be qualified and it should

also route it to the agency email. So if

I now go

submit, we go back into NADN, we go into

executions, we can see this one is

running. If we go to

Gmail and I pull up my spare

inbox and there we go. We see it has

succeeded. And then if we go

to and then if we go to our lead

qualifier and notifier and we go to

executions, we will also see that we

have a new one that has succeeded here

which was just uh a few seconds ago and

that's gone through. It is um outputed

it as agency which is the the

classification that we wanted. It has

gone through and has sent a new email.

And if we go to here we have new agency

lead there. There we go. All the

information. So that's working number

one. Now we can go back to our form and

we can try it again but this time with

let's say an unqualified business. Let's

go. What is your name? Ray Croc Ray

McDonald.com. Um

McTum I need more guys more people

flipping damn

burgers. So essentially Ray here has

come to our recruitment agency and

they're asking, "Hey, I need people to

do flip patties for me in my fast food

restaurant." Um, and because Big Boy

Recruits in Dallas, which is a

hypothetical company, of course, um

doesn't do that. It's going to qualify

them or it's going to disqualify them

and then send an email to our good

buddy. Oh

no, some poor dude at McDonald's is

going to get an email now. Um, because

send an email. Um, but it's going to be

running and of course it's going to be

sending. if they're unqualified, it will

send an email to them and say, "Hey

sorry you're not a good fit for us. Let

us know if we can help or we can connect

you to our

partners." And while that is running, I

would just put together the final one

here to test the functionality, which is

if we go Liam

admin.com, and we set up my SAS

https, my SAS agent, if you haven't

already used it, we're going to show you

how to use it in the last tutorial of

this video. So, you guys will get to see

that. Um, which is my own no code AI

agent building platform. And what can we

help you with agents? Um, so this should

be a SAS one and it's going to qualify

as SAS. Um, we

have the McDonald's one has succeeded

here. And you see, yep, as expected, we

were not qualified. The McDonald's

person was not qualified for our offer.

So, it looked at the qualification

criteria we provided in here, said

"Hey, no, that's not a good fit." So

I'm going to use this tool. And you can

see that it sent the email and it said

"Hey, thanks for your interest. Um, but

we're not a good fit for you." So

someone at McDonald's just got an email.

Apologize for that, but we didn't

trigger the other workflow, which is a

key part. And we're not going to send

emails to our sales team saying, "Hey

look, new leads." Now, I have sent

another one through here which just

finished

executing. And we can see this. It's

gone through. It's researched um

agentive. you'll see um Agent is a

leading service delivery platform for AI

agent AI automation agency owners um etc

and it's called the tool because we were

qualified because we're a SAS business

right and again if we go back to

here and we look at the most recent one

which is this oh we have another one

here then you'll see new SAS lead has

been triggered because we are a SAS of

course um the LLM step here has outputed

just SAS So that means that it should

send an email to the SAS team, which if

we go to my

inbox, tada, new SAS lead, right?

So I know that may have taken a while

but uh we got there eventually. And you

can see that we've built out all of this

functionality. We have our AI agent

calling our tools if they are qualified

and triggering this other workflow.

Again, you can build so much cool stuff

by connecting an agent to multiple

different workflows. We have a little

relevance AI researcher tool that we're

reusing here and we have people getting

denied um with an instant email sending

them back. So, hope this been a cool one

to show you how NATM works. I really

really like this agent functionality

that they have. I think you guys are

going to be able to build some awesome

stuff if you keep going down this rabbit

hole. So, that has been agent build

number two. Stick with me as we jump

into agent build number three, which is

a pretty damn cool one, focusing on both

chat and voice-based agents all in the

same build. So, let's get the ball

rolling.

All right, so that is two builds out of

the way. Well done if you made it this

far. We have another big one here. Um

this is going to be breaking down how to

use voice flow. Let's take this off

here. Um, to build an agent that is

going to be both accessible through a

website chat, so you can chat to it on a

website as a as a chat widget, which

you're probably familiar with, but we're

going to connect the exact same agent

and exact same functionality that you

get through that chat widget also to a

phone number on the website that will be

able to call and have the same

experience. So, this is going to show

you how on voice you can build both chat

and voice experiences. Um, and this is a

new feature for them as of the time of

filming. And this agent is what we can

classify as an AI customer support and

lead generation agent for both website

and phone. And we're going to build it

on voice flow, of course. And the

purpose of this agent is that it's

designed to be able to answer common

questions from potential customers via a

website chatbot and also via a phone

number that can be called. Not only can

it answer questions to help them sort of

move them towards a uh a purchase, but

it can also generate instant quotes for

interested parties. This is intended to

increase the number of leads that they

get because people who see a contact

form may be like, "Hey, I want to get

instant response. I want to know

instantly how much this is cuz I'm

shopping around." Um, and rather than

just filling in a contact form and

waiting. Um, having this instant

quotation can give people confirmation

on the price. Um, so they're ready to

take a step forward and end up getting

the sale ultimately. So that instant

quotation feature is a cool one. Um

very easy to do with the custom tool on

relevance that we're going to build. And

finally, this agent is going to be able

to actually capture lead information

from those who have been given a quote.

So after they've been given a quote

then we'll move to say, "Hey, give us

your details and we will follow up. Our

team will follow you up and set an

appointment for the service." And the

value here of the system is that

customers are going to often want

instant answers so that they can make a

purchase. So by offering easy ways for

them to get this information, we can

increase the chance that they're going

to purchase from the business. Um

companies typically have to spend money

on some kind of customer support or

sales team in order to get these kind of

answers given to customers when they

need them. But this agent can

essentially be a oneanddone solution um

to both help increase the sales of the

business by increasing that likelihood

of purchasing because they now have more

information um while also saving the

business money that they would typically

spend on some kind of support staff um

say if this chatbot can handle a dozens

and dozens of responses a a week that

would typically have to have gone

through a support person then we're

saving the company money and also

helping them increase their chance of

generating more revenue. So um here's a

rough layout of the design here. We are

going to have a website. I'll give you a

template for this. It's very easy to set

up and we're just going to throw in a

number um that's going to be connected

to the voice agent that we build and

we're going to be setting up voice flows

web chat widget as well. And this agent

is going to have access to a knowledge

base to answer questions um that

prospects may have about the business

and their services etc. Um it's going to

have a tool that is allowing them to

generate an instant quotation. So it's

going to take in some information. This

is going to be for a cleaning business

or a hypothetical cleaning business. And

then we're going to be able to generate

an instant quote for them based on the

property type and the size of the

property they need cleaned. and we're

going to be able to capture the lead

information afterwards and log it into a

CRM. In this case, we'll just use Google

Sheets, but it's fairly easy to swap

that out to whatever CRM you want. So

it's going to look a bit like this. I've

actually added a little bit more. And we

are using another relevance tool in

here. This is from a different project

from my accelerator, but I'm going to be

pinching that and putting it in here for

you all. And this is the uh tool number

two here, the generate instant quote.

So, we're going to be slotting that in

there, taking in some information

answering questions, etc. The process of

building on Voice Flow is one of my kind

of favorite experiences um in the

automation space. I really like the the

way they've built out their uh their

flow builder. Um so I'm sure you guys

will enjoy building this uh step by step

with me. And then the general usage

pattern of the system is that the

person's going to arrive on the website.

They'll either click to chat with the

chatbot and engage with this

functionality or they'll enter the phone

number into their phone. And then the

agent will jump in and respond either

through text or through uh voice and

determine what they're needing help

with, which is this section here. And

then it's going to be routing using this

router section here to the correct tool

whether they want a question answered or

they want to get a quote. Um, and then

each of these branches will execute on

that uh, functionality depending on

their intent. So, it's going to look a

bit like this. We'll have a phone number

and we'll have a chatbot like this. This

is actually an agenda chatbot from my

own software, but we'll be swapping this

out to a voice flow one in this build.

So, without further ado, let's jump into

voice

flow. So, when you click that link on

the Figma, it's going to take you to the

signup page. You can sign up there and

then once you're in, you're going to get

a page that looks a bit like this. The

first thing that we want to do, of

course, is to create a new agent up here

on the top right. Let's call this

Bonor's cleaning um, website. and phone

agent. Um, let's just start with a basic

template here. Import knowledge for this

import knowledge. We can actually just

skip that for now. And then we get into

the flow builder on voice flow. So just

a quick orientation if you are new to

the voice flow platform. This is where

we can add in our knowledge which we

will do shortly. The workflows are where

we access the flow builder. In most

simple builds like this, you just going

to have one workflow. So you don't need

to worry too much about that. Now we

have integrations like uh the widget

which we're going to be using to deploy

this on a website. We have the phone

number integration which we're going to

be doing later as well. Then we have API

keys etc which you don't need to worry

too much about right now. We have some

publishing features here which we'll

double later. We have access to

transcripts. So once we deploy this you

can access all of the transcripts either

by voice or through chat here and and

sort of dig through the answers and and

see how the uh people are interacting

with the agent that you've built. Um

something that a lot of people neglect

after they've put one of these into

production. And then we have things like

analytics um etc. But obviously we need

some data before we see anything useful

there. And then the settings page is not

too much you need to worry about right

now. Just sort of on a need to know

basis. The more important stuff, of

course, is up in this first tab here

which is content. So, we have messages

we have prompts, we have components.

We're going to be working a lot with

prompts shortly. So, that's the main one

we need to be worried about. But for

now, we can just go into workflows, and

we open up this first workflow and edit

it. And here we have the template that

VoiceFlow gives for us. Um, which I'm

actually just going to nuke this, and

we'll start fresh. And if you see on the

Figma, we have a design here that we're

roughly working towards. I'm going to be

showing you the sort of step by step.

So, we need a welcome step.

So, we're going to start off by going

here and dropping. I'll try to zoom in a

bit for you guys here. Talk message. So

message is how you send a message um via

the chat. So, start is when maybe you

click on the widget, it pops up. And

this message that we're about to put in

is the first message the bot is going to

send. So, we say um up here, hey, hey

welcome to

corners. I'm going to zoom this up for a

bit for you guys. And right away, we

have a little tip and trick that I want

to give to you because we are building

this as a chat and voice assistant. We

want to over voice. You don't want to

overuse punctuation because it leaves

these big long pauses when the the voice

agent is going, "Hey, welcome to

Connor's cleaning." So, we wanted to

just say, "Hey, welcome to Connor's

cleaning." A bit more natural. There's

times where you'll see me on the side of

sloppier punctuation, but that's just to

ensure that when we get to testing it on

voice, it sounds as natural as possible.

So, hey, welcome to Connor's cleaning.

And then, of course, we're going to wait

for them to reply and say something back

to us. We go to listen and then capture.

And this is going to capture the

information. We want to change this from

capturing entities which is like say I'm

looking for a price or an address. Um

we're just going to go the entire user

reply and the reply is going to be saved

into this variable here. So we actually

want to change this to um first user

reply because we're going to need it a

little bit later. Um the users first

reply. And I like to name these as we

go. So we can call this

welcome. Drag this out here. Get a new

one. And we're going to be doing a uh a

set step here. So, we're going to be

setting some variables. And I'm going to

add a new variable to set. And we're

going to do it based off a prompt here

which is a cool feature in voice they've

added recently. And we're going to be

able to select a prompt that's going to

take this information from this first

reply and then generate some kind of

output from it and set a variable. And

the variable we're going to set here is

called last response. So, this is

typically what you're going to put as

the last response from the AI or from

the agent. Um, last response here. And

last utterance is typically the most

recent uh message from the user. So

utterance is coming from the user the

most recent last utterance and the last

response is what the the AI or the agent

or the system has last responded to. So

we want to set the last response to

something that is generated through this

prompt here. So we can create a new

prompt here and this is basically going

to take in the data from the chat and

the conversation so far and we'll be

able to generate things off of it. So

say we add in here the conversation

history. That's a good thing to have in

in most cases. And I'm going to be

dumping in some of the prompts here just

to save us a bit of time. But basically

we're saying summarize the customer's

question below and ask them to confirm

that that's what they meant. And so

we're not actually going to be

generating the last utterance here.

We're going to be adding in the last the

first user reply that we got. I mean

it's going to be included here in the

conversation history, but there's no

harm really in hard coding it or at

least putting the variable in here to

make sure that it's in there. Um, we're

just saying summarize the customer's

question and basically say a

confirmation statement. So, just to

confirm this is what you're looking to

do. So, you imagine this over the phone.

Hey, um um yeah, I'm not really sure

what I'm supposed to be doing here, but

I was thinking if if you guys were

possibly so all of that information is

taken in, we can flick back to them and

say, just to confirm this, this sounds

like you're looking for this, yes or no.

Um so, ensure your tone is empathetic.

Speak directly to the end customer. Keep

your answer brief and two sentences max.

So, if we go back here and actually we

can name that prompt. So it's um

summarize

problem and then we need to send the

response. So this prompt is going to

take in the information we provided

here. It's going to use this prompt to

take in um the conversation history so

far and this information from the user

in the first question. It's going to

generate a a question to ask back and

it's going to save it to this variable

that we have here. So apply output to

variable last response. There is

actually an easier way to do AI

responses like this, but in our case, we

need to be saving this variable. So

it'll make sense in a second. But, we

can go into here and we can go last

response. And then it's going to send

that information back to them. So, let's

just do a quick test here. Click start.

Hey, welcome to Connor's Cleaning. Oh

actually, we need to ask a question.

Hey, welcome to Connor's Cleaning. Um

how can I help?

We'll

say I need cleaning services for my

house. Sounds like you're looking for

cleaning services for your greenhouse.

Is that correct? Want to make sure I

understand your specific needs before we

proceed. So, I obviously spelled house

with G house. So, we thought it was a

greenhouse, but that's what we want.

Some kind of confirmation message just

saying like, hey, look, is this what

you're actually looking to do before we

then go and trigger the different tools

that we are equipping our agent with? By

the way, there is a way of changing

between trackpad and mouse. So I am

panning around with my mouse here. You

can also do a trackpad method which is a

lot easier to use if you're if you're

having trouble using it. Okay. So after

that they're obviously going to say yes

or no whether like have I got the

question or have I summarized what

you're looking for correctly and we can

go to a choice step here and we can set

up some triggers. We can set the intent

to yes and then we can add another

trigger and we can set it as no. So this

is basically using AI to analyze what

they've said and grouping it around

these certain things which are called

intents. So, what is the intent of them

of this uh of the response? And in this

case, they have some pre-built ones, but

we are going to be building our own

custom intents later. But for now, just

know that if you're looking to sort of

split traffic or split people coming

through the system, these choice blocks

with the default uh intents from voice

flow, yes and no, are ready to use out

of the box. And if they don't say yes or

no clearly or we can't pick it up, we

can add a no match here. We can say

sorry, I didn't get that. Can you say

yes again? A yes or a no is enough. And

then we can say to follow a path after

these reprompts which we'll call no

match. And then we have this uh no match

path which we'll set up in a second

here. I'll just put it as a a filler for

now. Basically if if they don't say yes

or no um this is setting up error

handling. Um, and basically if people

particularly over the phone, um, there's

so many different ways that the

conversation can go and end up and

you'll want to, while I don't focus on

it too much in this build, um, as you're

building production grade assistants

you'll need to build a lot more of these

fallbacks and these reprompt and these

no match things to handle edge cases

where people use it in a weird way that

you don't expect. So, I want to give you

a little taste of that in this tutorial

but it is nowhere near representative of

what it takes to actually get something

into production that you can trust on a

on a customer's website. Okay, so we've

got this choice block set up to

determine if we have got their

summarization of the problem correct.

And we can take this up to here and we

can set another uh variable. And so this

is really the core part of the

application which is determining what

their intent is, what are they looking

to do and which tool are we going to

route them to it. So this is a very

handy skill to have which I'm going to

teach you which is how to set up some

kind of intent classification system. Um

which is really really essential to

building agents on on voice flow and any

kind of agent where the platform itself

isn't automatically handling that for

you. So if we go set a new variable and

we're going to do it through a prompt.

We're going to set a new variable here

called

desired action. So basically people

coming through and asking questions can

be saying hey look I I just have a quick

question about where you guys are based

and then we're going to route them to

the knowledge base. And then someone may

be saying hey how much does it cost for

this? And then we're going to route them

to the pricing uh the instant quotation

um system that we're setting up. So

needs to be able to determine what

they're looking for and we're going to

route them depending on that. And that's

what this router is going to do. So the

action that the prospect wants to take

the most likely action that the prospect

wants to take. Then we need to make a

new prompt here. We're going to call

this intent

classifier. Classifies the intent of the

uh prospect into asking the knowledge

base or generating an instant

quote. Add in the conversation history.

It's always good to have that in there.

And I'm going to put in the prompt that

I've written previously. And this is a

pretty basic one as well, which is just

saying what does the customer want to

do, ask a question, get a real-time

quote, or something else entirely. You

must output a label for this only. Your

options are ask a question, get a quote

or other. And you guys can just pause it

and see what I've got in there. But

basically, anything asking a question is

going to be about the business and the

services. And if there's anything about

pricing or directly related to getting a

quote and like they're ready to move on

this, then we're going to route them to

get a quote. And anything else is going

to go to other. And because in the next

step, we are going to be looking out for

either this is the output or this or

this. a really clear statement saying

this is all you need to output just this

and not hey I took a look at the the

conversation history and it seems like

the user wants to do get a quote we just

want just get a quote and we can

explicitly state that with this big caps

lock block here and as with the other

builds all of these prompts are going to

be available in the resources for you to

follow along with okay so now we get to

the cool bit which is routing this. So

if we go condition add this in here and

we go add path condition builder and we

say if desired action

is

ask

a

question. Oh that's that's all we need

there. So as you can see that's added

one in here. And if we want to add

another one in, we go if desired action

is um what do we have the label as

output? What did we what's the exact

label that we had in here? So this

prompt is going to be outputting these

labels. So we need to make sure they

match up. So ask a question. Get a

quote.

Yep. Get a quote.

and other one is

other and it's already got an else path

in there for any error handling as well.

So what this now allows us to do is to

build out our different tools. So we can

go up to here to ask a question. Just

throw this in for now so we can get an

idea of what it's going to look

like.

Um other this is going to be sort of

error

handling. And if it's else, that means

that the LLM step here hasn't outputed

any of the labels that we told it to

and it's likely thrown in a bunch of

rubbish. Um, so this is sort of an error

handling step. Um, should say, "Sorry

something went wrong, at least during

this prototyping phase. So now what I'd

like to do is make this look a little

bit prettier. Um, we can go through and

add things in here like this is the uh

confirm problem um intent classifier

router." And then we can go here add a

note can say tool number number one

answer from knowledge

base two you guys don't have this so be

easy for you guys to see understand I

know this might look pretty

confusing and then this other one we

don't need to worry about too much so to

keep things quick I'm not going to test

this just yet I'm going to test it once

we've got that functionality set up on

either side at least for this top one

first so now We need to go and set up

our knowledge base. And to do this, we

can click on the back button here. Go to

our

knowledge. And here we have a data

source which we can upload. I'm going to

upload a file, but you can put in URLs

to different websites, etc. I'm going to

be uploading a file

here. And I'm going to upload this

Connor's cleaning FAQ kind of document

which you guys are going to have access

to in the resources. Basically, it's

just about us, location, our services

ra um some frequently asked questions

etc. So, I've just AI generated this.

Um, and if you're doing any kind of

prototype builds, I recommend you do the

same just to throw it in there and see

if the knowledge base is working as

expected. Obviously, you'd swap this out

with actual customer data or or your

client's FAQ. I'm just going to throw

that in there for now. And you guys can

do the

same. And when you're setting up your

knowledge base, you can also set up the

settings for it. So, in this case, it's

using by default Claude 3.5 Haiku. And

you can see how many tokens this is

going to cost you for Voice Flow's

usage. Um, what is Haiku? Haiku seems to

be the cheapest. Oh, you've got GBT40

mini. Let me just chuck on GBT40 mini

here. We want this thing to be pretty

deterministic. So, I'd say 0.1 is fine.

Max tokens. Um, we can increase that

just in case it needs to give a longer

answer. And chunk limit of of three

should be enough. So, that's just so

this stuff is a little bit more

advanced. That's that vector database as

I was talking about. Basically

knowledge base is going to be sending

the message that we ask it and querying

it and getting back chunks of

information. Because our knowledge base

is quite small, we don't really need to

have too many chunks. If you put this

up, you just be getting the whole

document back. Anyway, so max tokens

the number of tokens that it's going to

include in the response. So, we want to

increase that to 480 um so that it can

give a longer response if they need.

Maybe just tone that down a bit. And

these are of course kind of controls

that you have on how much you want to

allow the app to spend. And those

settings obviously the main ways that

you can control how much um your

knowledge base is using and how much

your you or your client are ultimately

spending on the AI features for the

knowledge base. So once we've got that

set up, we can go back to workflows

here, open this up

again, and then to plug in our knowledge

base, we can go to the dev section here.

We can go to KB search, pop this in

here. I will

uh we need to delete that and reconnect

this up to the top. And we're going to

delete this as well. And so we can go

into this knowledgebased step here, and

we can enter the query. So what we're

going to say is we basically want to

throw the information that we've got

from the user already about what they

want which is we have here as the first

message they gave us which might be a

bit longwinded. Then we have the summary

that they have confirmed and then we can

put these into the query that's going to

be asking the knowledge base hey this is

what we want information on can you give

us some information back. So we can go

user first message put a curable in

there. We can go first use reply and

then we can also go summarized problem.

Now you can see why if we put last

response here, why we have this variable

saved instead of just sending it

automatically. So I actually don't like

using last response because that's

something that you like to update quite

a lot. So I'm actually just going to

switch this to um changing it to

summarized problem just so we don't get

any kind of overlaps that cause problems

down the line. A summary of the user's

problem.

Then we put this in here. Get to spit it

out. So when you put a variable in a

message, it's just going to print out

and and spit out the the value that's

inside that variable. So we've set the

summarized problem variable and then

we're just going to spit it out and send

it into the chat or over the over the

phone. So now we can come back out to

our knowledge base and we can take out

the SL response and replace it with

summarized summarized problem. Then we

can save the chunks that come back from

the knowledge base. I don't want to get

too in depth on what chunks are

specifically. It's a little bit more

advanced, but for now, we can just know

that it's going to return some

information from that knowledge base.

It's going to chop up that document we

put in. And when we put in this

question, it's going to basically ask

that knowledge base, can we get three

chunks that most closely match the

information that is in this query that

we sent to it. So, we can save these

chunks, which is going to be three

because we set that up in the knowledge

base settings into this chunks variable.

And the chunk limit is three still. If

we click this, we can add in a chunks

not found path. But for this tutorial

we don't need to worry about that

necessarily. And then we're going to use

those chunks that came back from the

knowledge base to generate an answer

based on the original question. So if we

put this here, we go talk, we go prompt.

And for this prompt, if we go here, we

can create a new prompt. We can add in

the conversation history just for good

measure here to give it the full context

of what's going on. And then I have a

prompt here. You are an AI customer

support rep from Connor's Cleaning

helping customer with the question. Use

the provider details below to answer the

customer's question. Ensure you keep

your answer brief and speak directly to

your end customer. You are speaking to

them over the phone. It's the input data

provider details which is the chunks

variable. I'll just put that in

again. Chunks to make sure it's set up

properly to the user's original uh

question. We can put all this

information back in. So, first reply

this is what they asked us as soon as

they picked up the phone or the first

message they sent when we asked them

what can we help you with. And then we

also just put in for good measure our

summary of the problem that they

confirmed. And we can go to our

summarize problem

variable and throw that in. That should

be good to

go. And I like to make these look purple

or some kind of cool color. Um, call

this a KB

query. And we can change this to

generate answer from chunks. Let's say

from Kh. And if you want to make this a

bit easier for you to kind of understand

at a glance, you can add in your

descriptions on these. So if we go to

edit again and we go here, this takes in

chunks from the from the KB and their

original question and writes a short and

sweet answer. And we have this in here

that you are speaking to them over the

phone because we want to make sure that

we're building this with the phone in

mind, which is more tricky than just

chat. So long text outputs don't really

work that well over the phone. So that's

kind of why we're putting that in there

as well. And we can call this um

generate

answer. All right. So now we can

actually give this a spin. We can start

it right from the beginning

again. Oh, we may need to if we just

click run. Okay, there's no training

needed yet. We can run

test. Where are you

located? Sounds like you're asking about

a business location. Could you confirm

if you'd like to know the specific

address or where Connor's Cleaning

operates? Okay, it's good this popped up

because as you can see, it's asking for

a non- yes or no answer. We're just

looking for a confirmation in yes or no.

And so this would technically break the

system and that'd be saying, "Oh, I'd

like a specific address." And this is

looking for yes or no. And so it would

send it to this no match. So what we can

do to fix this is to go into the

summarize problem prompt, modify the

prompt, and then say they should be able

to answer only with yes or no. This is a

confirmation step, not asking for more

information.

So now if we run that

again, whereabouts are you

located? Sounds like you want to know

the specific location of our cleaning

business. Is that correct?

Yep. Now it goes to the router here.

It's going to determine that I said yes.

Bam, bam, bam. And there we go. So that

that all happened pretty quickly, but

you can see it's sort of broken down

through here. Um, if I click over here

it's going to remove all of this. Okay

so let's break down how this happened

step by step. Um, so you can see this

through here. It's still using 3.5 haiku

for some reason. I'll need to double

check why that's still using the model

we didn't select. But basically, it

comes through this step here is the

intent classifier. So you can see that

it set it as yes. That is the correct

intent and predicted intent yes. And

that routed it to

this. And then using the model again, it

analyzed the information that it was

given. And then it set the desired

action variable to ask a question which

is one of the labels that we wanted and

that is correct. And then it said

condition matched taken path one. So it

set the desired action variable to ask a

question. We were checking for it here.

Then it said okay great. Now I'm sending

it up here to the knowledgebased query.

It says it's query received. We passed

in all the information whereabouts you

located and then the summary that we

gave it. And then we got two chunks back

from the KB and the AI response here

finally took in all of this information

and it gave us the final output and

generated this response. We are located

at 247 ra and at the end here it's

saying is there a specific area you're

interested in. For a basic build like

this I'd probably change the prompt to

say don't ask another question because

in this case you then need to set up a

looping mechanism where it can keep

answering questions for them and then

break out into any of these other

intents um as needed. But for now that

is a knowledge base and that's how you

can ask questions. And so that is tool

number one knocked out which was easy

enough. So great we can go on to tool

number two now. So for this second quote

I'll be able to give you a relevance

tool that we're using for it. But let's

just jump into answering this question.

So they've said here that they wanted

something related to pricing or quoting.

Remember in here in the router we have

set the intent classifier to say it's

going to go to the get a quote if

they're asking about pricing or have

directly requests a quote. And this will

take them to a real-time quotation tool

that takes the property type and size

and then returns an estimate. So that's

the people that are going to be getting

to this next branch of the agent. So

desired action is get a quote. We can

say, "Okay, sure." To give you an

instant quote, I

just Okay, sure. To give you an instant

quote, I just need the properties type

and size and square feet. Then I'm going

to add another chat step or message

sorry. And we can ask a

question. Is the property a house or an

apartment? The next we can do one of

these choice steps again and this time

we get to create some custom intents. So

we can go to triggers here and we want

to select an intent. See it doesn't have

a house. So we can go create an intent

and we say house. The

users property type is a house. And then

we can add in some examples here. So

obviously house this is just giving

examples to voice flows AI engine to

help us better to classify the different

intents as they come into this step. So

we can say home and then you can add in

some AI generated ones here which

usually pretty easy and uh which usually

pretty good. Okay, residence, dwelling

property, abode, living place. Uh this

this gets tricky um because some of

these could overlap with apartment. So

property is probably abodess too broad

living place.

uh dwelling uh

residence potentially we could get away

with. We could say like

manor mansion family

home single family

home and by now we've given enough

examples where we can just go to create

and now we need to do the same for

apartment. So, we'll go to create an

intent apartment. The user's property

type. The user's property type is an

apartment. And then we can put an

apartment. Um, see what else it's got

for us. Um, townhouse

penthouse, duplex, flat, probably not

right. Loft probably not right.

Townhouse penthouse. Think that's a good

bunch.

And then we can add in a no match here

as well.

Um, and we can add in a reprompt. Sorry

I didn't get that. Is it a house or

an If it is a house, we can come up here

and set it as logic here and go

set. We can

go value or expression. Select the

variable to set. We're going to go

property

type the

the save that variable or create that

variable, sorry. And then we can enter

this

in and set this as

house. So we're setting the property

type variable to house when it's been

triggered um by this particular

route. And then we need to add another

one. So, I'll probably just duplicate

this. That's by right clicking on one of

these blocks. Um, and then we can

connect up apartment to

it. Property type. Instead of being

house, we can make it

apartment. And from here, now that we

know the property type, we can set the

size of the apartment or the house. So

we go and how many

square and how many square feet is it?

We can connect both of these up here.

And then we're going to save the entire

user reply or on a capture step. So

anything that they respond to after

this, we want to capture the entire

thing. And we want to go here, set up a

set

step. Sorry, that's not right.

using a prompt. And what we're going to

do is use AI to analyze the response and

then extract the number of square feet

from it. We could have used a step here

where we change this to instead of the

entire user reply, we change it to an

entity. Um, but it's not as reliable as

doing it this way. So, I'd prefer to

just get it right the first time. Um

cuz a entity of the number of uh number

of feet may be a bit harder to pick up

than a clear word like an entity of

house or apartment or a name, etc. So

just to make sure that it works every

time for us, we want to create a new

prompt. And I'm going to put this in

here. Extract the number of square feet

from what the user said in numerical

format only. Each 500 include nothing

else in your response. This will be

saved as a variable and passed to an API

to a quote generator function. So giving

it a bit of context about what's been

going on. And also put in the memory

there just for good

measure. Oh, we didn't name the prompt.

Call this extract square.

And then we need to set the variable

that we want to save this to, which is

going to be proper

property size. So now we're saving that

response, the size of the

user's, the size of the user's property

in square

feet. And then we need to do one kind of

tricky step, but it's just something

that you guys will pick up as you go and

uh as you build more of these. But the

next step after this is going to be

sending that information to a relevance

tool. And the relevance tool is

expecting not what's called a string

which is just a number of letters or

just text. Um, you can have a number as

a text, which is confusing, but

basically it's just the format uh in

which it's being received in. So

because we're sending it over an API, we

need to be specific about the format. So

I can't take this is essentially going

to be saving that 500 or say if we say

it's a 500 foot property. This is going

to be plucking that out and giving us

500 as a string. In order to get the

response we want from relevance we need

to convert it to a number and then send

it. So a little block of uh custom code

here. Um know this was a no code

tutorial but I hope you can forgive me

for

this. And all we need to do is put this

in here. So property size this is the

variable. We're going to go property

size and we're just casting this

variable as a number and then

reassigning it to the variable that it

was before. So we're taking whatever

came out of this, we're saying, okay

can you just make it a number um and

we'll save it and sort of overwrite the

existing

variable. So now we have the number 500

in that case that we're ready to send to

the next step. And then if that's gone

correctly, we can talk to them and

say one sec while I get

And then we have this JavaScript fail

route here which you can just sort of

throw down there for now. Um there is a

chance that this prompt outputs not just

a number. As you can see we're asking it

for just this and therefore the property

size variable if it just is 500. Um but

sometimes it can say hey your size is

500. and then we end up with a variable

that's not actually convertible into a

number. Um, so we do need to add a

little bit there as a as a potential

fallback. Um, probably won't be doing it

in this video if I'm honest. It's fairly

basic. Um, but you would add this in

here some sort of looping back in to

make sure that it is actually in the

right format or just make sure that your

prompt is actually only you put like a

strict instruction only output just the

number um so that you get less errors

there. So the next major step after this

is to get our relevant AI research. If

we go back to the Figma here, you'll see

that we have this relevant AI tool which

is going to allow us to uh generate the

instant quote. So, I've pre-made this

and I've actually sniped it from some of

my accelerator resources. So, if you

open this link, it's going to allow you

to clone this into your relevance

account. So, up here you can click clone

and we'll just clone it into Morningside

AI. And now this thing is going to be

taking in a property type and a size and

square feet. And then we have a basic

LLM step here. This is a really really

simple one. Again, like I said earlier

in this video. The building really

powerful and advanced functionality does

take a lot longer. And if I was to do

things that weren't just a very basic

LLM step like this, then it would take a

lot longer with different platforms you

have to sign up for. The idea is to

teach you how these things can connect.

And then you can come into relevance

here. Once you know how to connect voice

flow to relevance through an API call

you can come in here and throw whatever

the hell you want in and make it as

advanced as you want. But in this case

we literally could have done this in

voice flow if I'm being fully honest.

But the idea of being able to access an

external tool via an API is really the

skill that I'm trying to teach you here.

So, what this is doing is taking in the

property type. We've got two options

here and the square footage in a number.

See here, it's it's a number. I can't

just type in here. It has to be a

number. So, that's what the API is going

to be expecting. You can see here that

it says it's expecting a number. And

then we pass this into a basic prompt

here that's saying the customer is

requesting this um and these are the

rough prices for the different square

footage and different types. and the LLM

in this case GPT4 mini just going to

look over that take in their inputs and

then give an output. So if I just go up

here and I set it's an apartment and I

mean I don't know what five how big is

an apartment in square feet I don't know

um but we can run

that it's going to give us some kind of

output and then it gives us the

estimate. So it's saying regular

maintenance cleaning for 60 deep

cleaning for 120 move and move out etc.

So that's the output that we're going to

be sending back to voice flow and we're

going to be turning that into a nice

message to send through chat or through

the phone. So in order to set this tool

up in voice flow and be able to interact

with it, we need to we can hide this

make sure the tool's been saved and you

have it in your account and then you can

go to use here and just like we learned

in theuh NA10 tutorial we can go to API

here and then we get an API for us to

use and again is a post request and it

tells us how to use it. So we have

params basically the inputs it's

expecting of the property type and a

string. You see how it's got two uh

quotations? That means anything inside

it is a string. And the square footage

here, you can see it isn't in quotation.

So, it's not a string. It's in this

case, it's a number. And of course, we

have the project ID here, letting

relevance know which tool that we've

created that we're actually trying to

interact with. And so, all we need to do

now to interact with this and set up our

quote generator is to copy this link.

So, this is the endpoint URL that we're

going to be calling over the internet.

And this is what it's expecting in the

in the body of that post request. If you

go back to voice flow here and we need

to go to dev

API, we're going to change this to post.

Put in that

URL. And in this case, seems that we've

got a little bit of a different page on

relevant for some reason. So we can

actually do the authentication step

which is helpful. So you can click

generate API key here. May have changed

by the time you're watching this video

but should roughly be the same. And we

can click deploy here. Make sure that

the API is up and running for us to

interact

with. Actually, now that we've deployed

it, we don't need the authentication

anymore. That was a bit strange. Uh

actually, I will do the API key just so

you guys see how this works. We can make

it private here. So, now that we have

our API key, we need to see how it's

expecting to receive that API key via

the HTTP request, which we're going to

be sending from voice flow. Um, so if we

scroll down here, curl is usually the

one I like to go to. And there is a

little bit I haven't really explained in

terms of headers and bodies when it

comes to API calls. But for now, just

know that when you have a curl request

like this, maybe it's even easier on the

uh JavaScript. Here we have what's

called headers. And this is basically

like the the the envelope that you put

the information in. So this up here is

the information that we're putting in

the request. It's going inside the

envelope. And the headers and the method

and the endpoint URL are like the stamp

and the information that you put on the

outside of the envelope to make sure it

gets where it wants to go. So you can

say that this endpoint here, that's what

we call the endpoint. Same as up here.

That's the the same as we have just

here. Endpoint is like the address where

the envelope is being sent to. We have

the post, the method. Maybe this is like

super fast mail or like overnight

delivery or maybe like a parcel versus

an envelope. Basically, the type of

delivery that you're doing or type of

request. And the headers include

important stuff like the type of content

that's inside it. So you might say this

like this is there's written text or

there's a letter in here. So it might

seem a little bit complicated, but we'll

fill this all in. Now, anytime you are

making an API call, you'll see these

headers around and you'll see the

endpoint and the method and then the

body which we're going to be setting up.

So, this will all make sense in 2

seconds, but let's just say for now we

have content type and it's going to be

application JSON. That's one of our

headers. So, if we go back to Connor's

cleaning, we open up the headers. We can

go content type here as we saw here

right? So content type, we need to set

it to

application/json. Very common one that

you're going to be

using. And we have another one which is

authorization. So this is basically the

majority of the endpoints you're going

to be seeing which is authorization and

content type. And a lot of the time it's

going to be an API key and application

JSON. So then we can go back to

relevance. We can get our API

key. And now to set up the body of the

request which is this. We can copy this

and change this to raw. And we'll paste

this in. we have the params or the

inputs in this case, the property type

and square footage for the tool. And

what we want to do is insert the

variables that we've got over here into

these. So we can go inside these

quotations here and we can go property

uh type in this case and then for square

footage we can go

property

size. And so because the property type

is expecting a string as we can see if

we go back here um to build

It's expecting a string and this is

expecting a number. The string must be

wrapped around in uh these quotations

and the square footage is a number and

so we can just put the number because

we've already converted it into a number

here. Right? So that's probably the

trickiest part of all the stuff I'm

going to teach you today. And now that

we have this set up, we can actually

send a test request and we can say uh

house. We can say 500 and test this API

call. And there we go. It's complete. We

have got back our answer the estimate

etc. and all of the information is

coming back from relevant AI as expected

and now what we need to do is extract

the information which we want which is

this answer from this API call and from

the response. So we sent a request and

we got a response. Remember how we were

using those terms before. We can click

on answer and we can save it to and we

can call it uh

raw quote

data because it is quite raw

um raw quote data

from. And now we have the information

back from relevance AI. All we need to

do is make it pretty and use AI to

generate a message that summarizes the

quote to the customer. So we can go a uh

prompt step here. We can create a new

prompt. Let's add in the conversation

history for context. And I've got this

prompt

here. Write a short and clear

explanation of this quote for the

customers. uh we can go

property property

type for the customer's property type

and we can put in the raw quote data

here. So that's going to insert this

with the customer's apartment or the

customer's house and then we're just

going to dump in the raw data that came

back from relevance AI. Your response

will be read over the phone. So it must

be all in one paragraph and no longer

than three to four short sentences. It

should read like and I've given an

example here of how I wanted to give the

outputs.

And so we can change this to

quote quote response. And now we are

ready to give this a spin. So if we run

this whole thing from the top

um

run, how much for a weekly clean? So I'm

going straight to the asking a question

about price. You're asking about a

pricing for our weekly cleaning service.

Is that correct? Yes.

Bam. Bam. Okay, sure. To give you an

instant quote, I just need the property

type and size and square feet. Is the

property a house or an apartment? It's a

house. How many square feet is it? 500.

I don't know. Is that a big That sounds

very small to

me. I don't know if 500 I have I have no

I have no clue about the sizes of houses

if 500 is normal or

not. And there we go. Based on your 500

foot house, we have four cleaning

packages. Regular maintenance cleaning

at $90 for standard weekly cleaning

deep cleaning, ra. So, we're giving them

a quick summary based on their 500 ft

house or x number of square ft x

property type and giving them a quick

summary. So, that's pretty cool. We're

using relevance tool and we're getting

this information back. Now, there is one

more step that I have added onto this

this I get this thing where if I've gone

this far with you guys, I may as well

add in like the rest of it to make it

actually a bit more useful. um which is

a quick lead capture using Google Sheets

and Make.com. So, I couldn't leave you

guys hanging on this. I thought I may as

well throw it in there. So, stick with

me because this is really where you're

going to be like, "Oh, this is this is

uh opening my eyes to to what you can do

with these kind of platforms." So, the

reason we're adding this on is because

the person has asked about pricing or

they're directly interested in some kind

of services and we've given them an

instant quote and now we're trying to

immediately follow that up with, hey

look, give us your details and we'll be

in touch and we'll get that service

booked in right away. So, we can jump

into a uh message block here. This is

I'll just paste this in to keep things

nice and quick here. Please provide your

name and phone number and I'll get one

of the team to call you to find a time

that works. Now, one thing I will change

is as you saw on that last run, it had a

question at the bottom. It's like, which

one are you interested in? I would

probably try to remove that.

Um, do not ask a question at the end.

Just give them this description.

So that's just going to end the message

and saying, "Look, this is a quote."

Bam. And then the next message I get is

this. Please provide your name and phone

number and I'll get one of the team to

call you to find a time that works. And

so we want to save this entire user

reply with a capture step. Capture step.

There we go. Change this to entire user

reply. So we want to capture all of the

information that they send or over the

phone or either through chat. So in this

response, they're going to say their

name and their phone number, right? and

we need to extract those

out. I'm going to put this here and do a

set step. We're going to use a prompt.

So, we're setting this with

AI. And we're going to create a new

prompt. Um, let's just set the variable

first. Let's say this is going to be put

into last

response. Make a new prompt

here. And this is the prompt that we're

going to be using. So, we have just

asked the user to provide their name and

phone number. We need to attempt to

extract the information and then confirm

it with them. Here is their reply last

utterance which they've just provided

and we've captured. If there is a valid

name and phone number present, then you

must do a confirmation eg okay quickly

to confirm your name is this and number

is this. Is that correct? However, if

one or both are missing or appear to be

invalid, you must output only retry as

your response and nothing else. This

retry variable will be checked and if it

matches exactly, then it will trigger

another attempt to capture. So either a

write a short and sweet confirmation

message or b output retry for another

attempt at capturing. So what we're

doing here is using AI to analyze the

response and say look we're looking to

pluck out a name and phone number and we

want to also confirm that cuz this is

likely going to be over the phone. And

if the AI doesn't see a clear valid

phone number and a valid name then it's

going to output only the word retry. So

give us a response. If it's good to go

and we can move on to the next step. If

it says retry then we're going to try to

retry. Um, now of course this retry

doesn't actually do anything unless we

build the functionality in to look for

that retry keyword, which we'll do in a

second untitled prompt. We can change

this to um extract what attempt extract

name. And then we can go to um

conditions here and

go if last response which is the

variable that we're saving the output of

that prompt into here. Um whether it's

going to be retry or the say just to

confirm is this your phone name and

phone number. Um if last response is

retry or even just to make it a bit more

flexible contains retry unless unless

the person's name is like Bill Retry

Smith then uh this should be fine. And

actually, just to make this look a bit

cleaner, I might change this to um if

last response um does not contain retry.

Then we're going to go up

here and we are going to send the last

response because as we said, it's going

to either generate the confirmation

message or it'll output retry. So if

it's valid, it'll and we put up last

response here. They'll say their name

and phone number. We'll analyze it and

then we'll go great. It doesn't contain

the word retry in it. And bang. Hey

just to confirm this is your name and

this is your phone number. And then for

the choices here, we can go and use our

handy dandy. They're going to be giving

us a yes or no answer to this. So, we

can put in these two triggers, yes and

no. And so, if they say no to the

confirmation, say no, that's not my

correct phone number or name, then we

need to have some kind of retry um logic

here. I usually like to make my retries

um a orange color. And then my failure

is a red. Um, so we

say, "Okay, let's try that again. Can

you please give me a full name and phone

number, please?" And then we're going to

send them all the way back to this step

here. So they're basically going to

recapture their information and then put

them through this process. And this is a

loop that can be done over and over and

over again. So, and then we also need to

deal with this else step. So if the word

does contain retry and it has said

"Hey, look, this isn't a valid phone

number or a name," then we need to deal

with that as well. So, we can come down

here and go to message. Pop it under

here. And I've got a message for this.

Sorry, I didn't quite get that. Can you

please give me a full name and phone

number so a member of our team can get

in

touch. Um, right click on this and you

can go block color. Change it to an

orange. And then we're going to be going

back to here as well. So, it's helpful.

You can click on these arrows. So, the

lines here, and you can change them to

the same color. So, we want to make them

a bit more obvious that they're coming

in from places we

expected. We can make this an orange one

as

well. And this one

too. So, if they come in and they say

"Hey, my name's Bill and my phone number

is

02111." And it comes in here and it

goes, "Hey, that doesn't look like it's

proper." It's going to send a retry as

the output. We're going to pick it up

here and it's going to say, "Sorry, I

didn't quite get that." and it's going

to come back up and they're going to be

expected to give it again and it will go

through and then once we got a valid

name and phone number and it's not

outputting retry, then it's going to go

through here. It's going to spit that

out and say, "Hey, just to double check

this is your phone number and email

before we proceed." Yes and no. No is

going to be handled there and it's going

to take them back to the first step

again. So, that's some nice um error

handling and sort of looping that you're

going to need to be building into a lot

of your conversational AI agents

especially on voice flow, right? And so

the last steps here are some quick

variable extractions. So we can go to

div logic here and go to set and we're

going to extract the name and the phone

number. So we'll go prompt. Holy moly

it is it's bloody hot here. The variable

that we want to save this, we were going

to be extracting the name. So we'll add

a new variable called name. The user's

name. Um, and this is going to be called

uh extract

name. Here's a quick and easy prompt.

You can pause it to take a look at that.

It's just going to extract their

name. We need to add another variable.

This is going to be their phone

number. And then we need another

prompt. Just paste this one in here.

Pretty basic. Again, pause it if you

want to take a

look. And I haven't named that prompt.

It's going to annoy

me. Extract phone

number. And now we're going to have

their name and their phone number

extracted out of this response.

And oh, and actually we need to add in

the conversation history there so that

it actually has the this information in

it. And then we

go great. Let me get that added into our

system. This buys us a bit of time as we

use our um make web hook which we're

going to set up now. So the next step is

to get a Google sheet set up and to use

make.com to uh take this data and shoot

it into a Google sheet. So to do that um

I will leave a link on I mean you can

just search it up. It's make it's

make.com. All right. I'll save you the

hassle. So you sign into make.com create

an account whatever you want to do or

need to do.

Go to scenarios and then we're going to

create a new

scenario. I'm going to build from

scratch here. I'm going to get all that

rubbish out of the way. I don't know why

it's acting like I'm some rookie here.

Um, and then we need to go to web hooks

custom web hook. We're going to

um we're going to add in a new web hook.

This is going to be conors cleaning lead

capture. going to save that. It's going

to create this web hook here. I'm going

to copy this edges to clipboard. We're

going to come back to our build and then

we're going to go to

the API step. So, what this is doing if

you're a bit new to to web hooks and and

API calls and stuff. What we can do here

on make is set this up to basically

listen. It's a URL. You know how we had

the endpoint? The end point, this thing

here that it's given us that I've just

copied to our clipboard. That's like the

address, remember? So if you put it

write it on the on the letter, that's

where it's going to go. This allows us

to basically send data um via API call

um from voice flow to make and it's

going to catch it here. And this little

lightning bolt means that anything we

build after is going to be triggered

whenever it receives one of those uh

whenever a new bit of mail arrives. It's

going to then trigger this multi-step

process. So we're going to put this into

voice flow. We're going to trigger it

and make sure it knows what data to

expect. And then we're going to be able

to use that data and put it into Google

Sheets. So stick with me here, but this

is another very very essential skill in

AI automation is how to set up a webbook

um and use it within different apps. So

we have our webbook

here. We've copied the address to

clipboard. We're going back setting up a

get request here. So it's not a post

request this time. We're just uh getting

and we're technically not getting data.

A get request is a much more kind of

quick and dirty request. Um and as

you'll see, we kind of just tack on a

bunch of information after this. Um we

can do it through what's called

parameters. Here we want to be sending a

property. Oh, let's just say

property

type. Actually, let's do this properly.

Let's go time

um

timestamp. So, in this Google sheet

you're going to want to know when the

different leads came in. So, we can go

uh time stamp and get the time stamp

from voice flow. That's a default

variable that they are automatically

filling out for you. So, one of the

things, one of the rows in the

spreadsheet is going to be uh the time

stamp. We're going to add another and

we're going to go um name. We're going

to put in the name here. So, now all the

cogs in your head should start turning

as we put this together. And we're going

to go uh phone number and we go bracket

phone number

number. And if we add another, we can go

property

type. Let me go prop property

type. We can go

property

size and go property size. We can add

another one. We can go

quoted prices so that the sales team

knows what we actually told them. in

case you're playing around with pricing.

Um, raw quote data. And I'll probably

throw in one more here, which is their

first question. Um, first question

uh, user first reply. So, that might

give context to the sales team like what

did they actually contact us for in the

first place? Maybe helpful, maybe not.

But we can just send this all over to

the Google sheet.

And so now we can see this is uh as this

thing's spinning around, it's basically

waiting for us to send some data to it.

It's basically sitting there at the

mailbox like waiting for it to come

through. Um and we can go send here and

I'm going to put in uh gosh dug myself a

hole here. Um let's go name

Liam. Um house probably size. Um

um lots of money. Um how much for

cleaning yarning?

um send. And if we go back, bam

successfully determined. And what we've

done and determined means is that make

has received the the the request that we

sent. And it now it knows that we're

going to be sending it a time stamp and

a property type and this this really

really key skill to understand because

now when I go oh save now when I go to

here and I go Google Sheets and I go add

a

row, um you will need to set up your uh

Google Sheets connection here. So, you

just sign in with Google, add your

connection in. Um, I am going to have to

create a new spreadsheet for this

quickly. I'm going go timestamp.

So I'll zoom this up. Time stamp name

phone

property type property

size. 1 2 3 4 5 6 7. Yeah, we've got

them all. Right. So then I can

go. So call this my Connor cleaning

support agent leads.

So now I want to go back to make and I

want to click here to

um Connor. There we go. Connor's

clearing. Why do I keep spelling

cleaning

cleaning? Now we have the spreadsheet

set up. The sheet name is just going to

be sheet one. Sheet one. Does the table

contain headers? Yes, it does. And now

we get to put in all of our values. So

bam. Time stamp. Pop that in there.

Name, pop that in there. Phone, pop that

in there. Property type. So, you see

what such a key skill that uh I really

really want you guys to learn. Um

because this is a lot of like if you

just have a couple of these key things

using these APIs and say voice flow or

in NAD, building tools and relevance and

then using them via API, knowing how to

set up web hooks and then send data in

between them and to set this data

structure. You send that first initial

batch over to make in a test test

request. It's going to go okay this is

what they're going to send me in future

and then that's locked in and you can

send thousands and thousands of requests

through it and it will operate as you

expect and first

question. So uh it's got all those other

rows we don't need to worry about. Save.

All right. And so that should be

working. We can switch this to

immediately. Oh, I need to save it.

Save. Um I'm going

to immediately as data arrives. So, this

is going to be waiting all the damn

time. Actually, I'm I've got a whole

bunch of other stuff running in make for

my my various businesses. So, I'm just

going to have this set up. If you wanted

this to run around the clock, you turn

that on. I'm just going to turn it on to

run once. And this is just going to be

sitting there waiting for data. So, what

we can do is just finish this off

quickly. So, I'll just paste this

in. Got this all done. Thank you. That's

the end of it. Um, what we want to do is

have a failure path. So, in case this

fails, we just pop that down there and

say, "Darn it, something went

wrong." I'm going to put that as a

red. Um, and then this one is like that.

All righty. So, if we look at make, it's

still expecting us to send some data to

it. Let's hope that we've done all of

this right and it's going to work first

time, but you never really know, dear.

So, let's give this a run and we'll try

to go through that second path and we'll

try to get that stuff added into the

spreadsheet. I need weekend cleaning

please.

So asking a question is going straight

to price. You're looking for weekend

cleaning services and want to know the

pricing. Is that correct? Yes. So our

router is going to pick that up and say

he's looking for pricing. Let's send him

to the quote tool. Okay. Sure. To give

you an instant quote, I just need the

property type and size and square feet.

It's a property. Let's go aart

mint. It's detected that correctly. And

how many square feet? It's 500.

typical

apartment

size

and square

feet. Okay, so 500 isn't actually

ridiculous. Um, great. And then we get

our answer based on your 500 foot

apartment. Bam, bam, bam. There's all

the prices. Please provide your name and

phone number and I'll get one of the

teams to call you with a time that

works. Let's uh give this a a test. See

if it's actually picking up on the fail.

Sure. My name

is and

phone

is boom. Sorry, I didn't quite get that.

So, it detected that it wasn't right.

So, we got the retry output. We got the

retry output from this, which is what we

wanted. Sorry, I didn't quite get that.

Can you give me again? Um Liam

Otley number is um 021

021. That's what numbers look like here

in New

Zealand. Okay. Just to confirm quickly

your name is Liam Mley and your phone

number is that correct? Yes, sir. Name

and phone

number. Oh, what has it done

there? Oh, dame.

I don't know why it's I just want the

phone number. So, we have got a little

bit of an error there. I just go back

and tweak the prompt. Make sure it's

like only get the phone number. We don't

want anything apart from numbers here.

Great. Let me get the added to the

system. And then if we go back to make

we see these are all green now. And we

see these dots. So, this is the

information that came through. Namely

Mly phone number. And so, here's the

little mistake where we had a new line.

Apartment. Bam, bam, bam. All of that

information. And then it's added it into

Google Sheets. Here we see updates

updated number of rows, all the values

that it's updated and we can go

to. Okay. Holy moly. Right. We're ready

to put this thing on our website and to

also put it on a phone number. So, let's

just finish the job. Guys, I'm uh

getting real hungry, but we can uh we

can push through. So, I will just turn

this on actually so that if we are

testing it on the web and over the

phone, um it's ready to receive. Um, if

we do want to be pedantic, I would go

back and I would change I have to do it.

It's going to piss me

off. I'll put their name. Oh, that's

why. I'll put their phone number only.

My bad. I'll put their phone number

only. And we do have these fail points

here. Um, I'm not going to bother

filling them out. I think you guys can

figure out based off how I've handled

this, how you can handle these as well.

So, what you'll find is when you're

building these, these kind of fail like

error handling um is kind of a an

endless thread that you keep pulling.

It's like, oh, well, now I've got to

handle this, this, and this this. So, um

I'm not going to this is a prototype.

I'm not going to be doing all of the the

error handling for you here. In the

template, there is actually a little bit

more of it. Um some better examples. So

maybe if you import that, you can just

steal the work that I've done there. But

what we need to do now is we can publish

this thing. We'll call it

V1 first

drop. All right. So now it's published.

We can add the agent to a website. Let's

click on that. And that takes us to this

integrations tab.

Um let's put this down. I don't need to

see that. Um, they've got a new version

of it. That's good to know. I said

installation is pretty straightforward.

So, we can just click copy here. And

then I'm going to open

up I'm going to open up brackets here

just to give you a demo of a website. I

use this in all my tutorials. It's

really easy to spin up. Um, I will leave

a link to this template if you want it.

Um, and also some instructions on how

you can open up a website. I know this

looks like code and it's all scary, but

um, this is just allowing me to spin up

a website very quickly. So, I'll leave

the template file. All you need to do is

once you've downloaded the template

file, you need to download brackets

which is the software. You can go file

then open folder, and then you want to

click on the folder when you've unzipped

it, and it's going to open up the whole

folder. And then you'll get all of this

uh opened up like this. And you see all

of these files ra. All you need to do is

click on the

index.html. And then you'll see

something similar to this. Well, I'm

going to scroll down to the bottom of

the index.html. I'm going to delete this

old voice agent I was testing on here.

Drop this in here. Paste that. And then

save it. Command S. Click this little

button up here. And it will show us a

local version of the website running on

our computer here through brackets. So

here's my man with a magnificent

beard.

And we have the tester agent bubble down

here. And there we go.

I want to know where you

live my

guy.

Yep. Woo. Okay. Uh I didn't even program

that in there. Maybe you just thought it

was inappropriate. Um but we've got it

working on a website. Now, if we pop

back over to um uh Voice Flow here and

you go to the integrations, the widget

you see we've got this test your agent

thing. So, down here, we can play around

with the look and feel of it. I'm not

really going to get into this here.

There's quite a lot to play around with

but basically all of what you see on

here can be changed around. Different

logos, different text here, different

icon, etc., different colors, and you

can just make it look and feel however

you want it to. So, I'm sure you guys

are big enough and ugly enough to figure

that out yourself. we'd probably want to

switch over to uh this here. One thing

you would want to do is turn off powered

by voice flow so it's not uh sending

traffic to them when it's on your own

website. And that's about it. For the

sake of time, I'm not going to go

through the entire flow again here. Just

know that the functionality that we

built that I just showed you in the

builder is going to work cuz we just

deployed it. Like this is exactly what

we're interacting with. So, it's all

working here. The only step to do now is

to put it on this phone number so we can

have a chat to it over the phone, which

we're going to do now. To do that, we

need to go to the telefan bit here. It

is in beta right now, but for most of

you watching it is not going to be by

the time you you are watching this. So

we need to set up a phone number from

Twilio, import it, and then connect our

agent and its functionality to that. So

we can go import number. You'll see that

we have this information here. So, we

can use Twilio or Vonnage. Twilio is

usually the go-to here. So, if we click

on learn more, then they're going to

help us. Basically, the best way to make

sure you're getting the most up-to-date

information is go to the docs of the

platform. Finding and reading and

extracting information from

documentation on these kinds of

platforms is another key skill that you

need to pick up to succeed in the space.

So if we go to the docs here, we go to

um voice phone number setting up Twilio

integration and they have a video here

adding a phone number to your agent. So

if you ever get stuck, you know, you've

got documentation here and for all of

the other platforms, but they'll keep

updating these videos if things change

which they likely will as this voice AI

space really takes off. So, if we go to

Twilio, you will need to sign up and

create an account on

Twilio. All right, so we are logged into

Twilio. You'll need to create an account

for most of you, but Twilio is a uh

phone number provider that you can

connect to and interact with over the

internet. super helpful when you can buy

lots of numbers from different locations

and stuff. When it comes to phone

numbers, it can there's a lot of rules

and regulations around different like it

varies a lot from country to country. So

depending like if you're in Germany, I

believe in order to get a German phone

number, you need to have a company

registered and get the number through

your company registration and provide

those details. So can be difficult. I'm

just going to show you how to use a uh a

US-based number here. So we can go over

to the phone numbers on the left here.

There may be some setup that Twilio walk

you through. It can be kind of annoying

sometimes. They say you need to do all

of these declarations and forms and

stuff, but for the most part, it should

be fairly straightforward if you follow

their setup instructions when you create

your account to then come over and go to

your phone numbers and manage and go to

buy a

number. Now, unless you have other

purposes you want to use this for, you

can just snag any random one if you're

following this tutorial. Um, if you're

obviously doing this for a client, you

could get one that's matched to their

location or their their state or even

their city. And when you click buy, you

can see there's all these kind of

registrations and RAR you need to do.

But thankfully, voice is uh is ones that

don't need all of that. And you've got

global routing, etc. So, you can come

down here and buy this. It's going to be

a dollar a month. I know cost of

starting up a business is ridiculous

these

days. How dare they? But just to walk

things through and do it with you, I'm

going to buy this number even though I

really don't need

it. There we go. I've got my number. I'm

just going to copy this so I don't

forget it. 517. And then I go to active

numbers. I got my guy. Where is he? 517

down

here. Copy this bad boy. Go back to

Twilio. Put our number in here. Our

Twilio account SID. You need to pop back

to Twilio. You go to your account

dashboard. This was a bit confusing when

I first did it. So, I'm glad I'm glad I

can help you guys through it because it

was a bit of a pain in the bum for me.

You think all this information is going

to be like tucked away in the side

panels, but no, it's just on the

dashboard. Who the hell puts your or

tokens and stuff on a dashboard? I mean

it's when you know it's there, it's

helpful, but it was a bit confusing at

the time for me. Um, I'm going to paste

this in

here. We're going to copy this or token

and paste this in here. And we're going

to say this is my you YouTube tutorial.

And I'm going to import that bad boy.

Select environment. I'm just going to

put it on development for now. uh we can

assign

that. And with that, you can see if I

click unassign here, that means I'm

going to take it off. So, it's it's

assigned and it should be ready to have

a chat with. So, if I click to copy this

number here, I go back to my little

uh uh website code here. If I go find um

plus one. Oh, there we go. So, we have

the call. Um this is in the header. Uh

this is just me p wanting to put it on

the site for you guys. So, you guys

don't necessarily need to copy this. I'm

going to save that. If I go back to my

website here, now I have the actual

phone number. And if I pull up Skype on

my phone, you guys, if you're in the US

you can just or wherever your number is

based, you can just call it stage plus

one. All right, let's give our mate a

ring.

Welcome to Conor's Cleaning. How can I

help? Hey, uh, I was just wondering, uh

where you guys are located.

business. Is that correct? Yeah, that's

correct.

We're located at 247 Beacon Street in

Boston, Massachusetts 0216. We serve the

greater Boston area, including

neighborhoods like downtown Boston

Cambridge Somerville Brooklyn and

several others. Is there anything else I

can help you with today? Sweet. Okay, so

that's done the

uh knowledge the knowledgebased route.

Now, let's give it another spin. Call

him again. Okay.

Hey, welcome to Connor's Cleaning. How

can I help? Hi there. I'm just getting

interested in getting uh some cleaning

for my place on the weekends.

It sounds like you're interested in

booking a cleaning service for a

licensed weekend. Is that correct? Yep.

Okay. Sure. To give you an instant

quote. I just need the property type and

size and square feet. Is the property a

house or an apartment? It's a house.

Sorry, I didn't get that. Is it a house

or an apartment? It is a house, a home.

And how many square feet is it? It's

about a thousand.

One sec while I generate your quote.

Based on your requirements, we can offer

four different packages. Regular

maintenance cleaning at $90, which

covers standard weekly or bi-weekly

cleaning. Deep cleaning at $150 for a

more thorough top to bottom refresh

move in, move out. Cleaning at $170 for

preparing or cleaning after a home

transition, and post renovation cleaning

at $200 for handling construction dust

and debris. Please provide your name and

phone number and I'll get one of the

team to call you to find a time that

works. Sure. My name is Liam Otsley and

my phone number is 021 uh 1111 9792.

Okay, just to quickly confirm, your name

is Liam Oxley and your phone number is

02119792. Is that correct? Yep, that's

spot on.

Great. Let me get that added into our

system. All done. Thank you.

Boom. All

righty. That is done. That is dunzos.

All right. So, you guys learned a lot in

that one. voice integration, website

integration, um connecting web hooks to

make, setting up make automations

sending data over, connecting relevance

AI tools into into voice flow. Um basic

integration with a CRM, in this case

it's a sheet, but there's so much in

there, guys. I hope you really really uh

learned a lot from that. This has been a

big one. And we've still got uh one more

to go. So, I hope you're sticking with

us. Um but going back to our Figma here

um we have ticked off all of this. So

we have it as a web chat widget and we

have it as a a phone number. Now, as far

as I know, you can have both options for

the same agent on voice. You can have it

on the website and over voice. You don't

need to duplicate it and sort of define

what modality it's going to be. So

we've ticked off all the boxes for this.

All of the resources will be in here.

All of the prompts, a template for that

whole final build as well. If you just

want to snag all my hard work and go and

sell it to someone, again, I don't I

really don't care. Um, that's what these

videos are for. And we're getting into

agent build number four now.

All righty. So, last but not least is an

agent built on my own software. So, I

didn't want to make this. It's not about

me selling you or getting you to use my

software. So, I thought I'd put it at

the end just so you know that I wasn't

really This is about you guys learning

and my software happens to help you put

an agent onto WhatsApp very very easily.

So, that's why it's included in here.

But again, this is nonsponsored

nonpromoted, non whatever. I'm just

really trying to share with you what I

think is a really valuable skill set to

have. All right. Now getting into AI

agent build number four. This is going

to be tada a WhatsApp based ARI customer

support and lead generation agent built

on agentive my software. So this is a

noode uh AI agent builder that is built

on top of OpenAI's assistance API. So

you're technically using your OpenAI

account getting very very cheap rates on

the uh token usage that you're running

through this agent. But Agent just

allows you to build on top of it very

easily. but more importantly to deploy

these agents not just onto web chat

widgets like we've done with voice flow

but easily onto things like WhatsApp and

Instagram etc. So that's really the key

thing that Agenda focuses on doing right

now is making it easy for you to get

your agents onto these platforms. So as

you can see it's a fairly similar build

to what we just did on voice flow in

terms of functionality. We're going to

be having a uh a knowledge base that we

can ask questions over. It's going to be

able to generate another instant quote.

So we'll just quickly connect that same

relevance tool here. And finally, we're

going to do a lead capture, but this

time it's going to be done through Air

Table. So, I want to mix it up and show

you how you can connect your agents to

Air Table, which is a very, very common

integration that you're going to need to

know. And the difference between this

agent, you're going to see that it's

much much faster to build. This is not

meant to be a side-by-side comparison of

what's better, how much faster. It's

just that when you build on a more

conversationalbased uh AI agent platform

like agentive which is built on top of

the assistance API, it's a very

different way of building agents because

it's all just based on a prompt and

providing the right tools and all the

magic kind of happens itself through the

prompt. Whereas voice flow gives you a

lot more control. So it's really

difference between structured AI agent

building versus more conversational and

open-ended chats through more chat GBT

like experience that can just go on and

on and on which is what these agents can

do. So the purpose is of course fairly

similar but the value of this is

slightly different in that we are using

WhatsApp. So uh many people browsing for

services online are hesitant to use

website contact forms or other or chat

bots that they think are not going to

give them access to a real human due to

the potential delays that come from it.

Right? You land in a website and you're

you're shopping around for a different

service or product and then there's this

this contact form or there's a a web

chat widget and you're going to go well

I don't really think I'm going to get

the help that I really need here at the

at the speed that I want. So you might

look for a WhatsApp widget and you know

that if you click that WhatsApp widget

you're going to get to speak directly to

someone and this is kind of playing on

that fact that if you have the WhatsApp

option on your uh website people are

much more likely to just click that and

go through and try to have a

conversation directly to get what they

want. So by having a WhatsApp option on

a website or other triggers eg you can

have a QR code that you could stick on

say a real estate sign and you build an

agent connect it to your WhatsApp number

like we're going to do here and then you

create a QR code that people can scan

and immediately open WhatsApp and start

chatting with it. There's lots of

different ways that you can have an

access point into a WhatsApp agent like

this. But it basically opens up more

conversations through a more smartphone

native platform. So they can hop on

their phone and sort of have a chat away

to it rather than being on a website on

the computer or a little tiny website

chatbot on their phone. Um, in order to

essentially engage more prospects or

more people interested in the business

in conversation, quickly provide value

through either the knowledge base and

these tools here and real-time quotes.

And ultimately, because you're providing

that instant value and instant feedback

from them, collect their lead

information, or better yet, even set

appointments through WhatsApp, which you

can build on agenda. But that use case

is a little bit more advanced and not

something I can show within this video

but it definitely is possible. But it's

really only a few steps away from the

skills that you've learned in this video

so far. So keep an eye on that

appointment setting use case because if

you can do that with AI agent, it's a

very, very valuable one. And I've done

other videos on the channel here showing

you how to do that. So, the usage of

this is that they're going to find the

uh company's WhatsApp number on their

website or elsewhere, maybe a QR code

like I said, and they're going to start

a conversation on WhatsApp, and then the

agent is immediately going to jump in

and start responding and be able to

answer from the knowledge base, generate

quotes, and then capture their lead

information. So, without further ado

let's jump into building this agent. So

we can click up here to go to my

website, Agentive. You can click on

register now. You can just register with

your Google account. I'm going to log in

with mine.

It is free to make an account and we

have a free plan so you can just

experiment around as much as you need

and then you're only going to be charged

based on the amount of usage you use. So

it's very very cheap and affordable and

I wanted to make this platform for you

guys to all get on and experiment with

building AI agents without coding. That

was really the the core of why we

started this whole thing. So we've got

the dashboard here which will load in my

data in a

second. So you can see here what the

dashboard will look like when you've got

your own agents running. We are running

the Agentive customer support chatbot

through this uh through this account

that I'm I'm using right now. So you can

see usage costs very cheap sessions etc.

So it's really cool when you go into

analytics and you can use agentive to

see how people are using your agents but

that's obviously something for a little

bit later once you put these into

production. Uh now what we're going to

do is of course we can go to agents or

we can just create an agent from here

and I call this um Connor's cleaning

WhatsApp agent. Oh we got a little

description as well. um answers

questions from KB provides real

time so answers questions from the

knowledge base provides real-time

cleaning quotes and can capture leads to

air table. So you're going to see the

setup is a lot faster than some of these

other platforms. Again, I'm not not

trying to gas myself up here. It's just

a different way of approaching u

building agents. So it's a lot more fast

and and rapid prototyping and easy to

get things up and running. Of course, if

you need much more advanced

functionality, you do need to go the

extra mile and go on to platforms like

Voice Flow. But in this case, we have a

prompt, very easy. We have a knowledge

and we have tools. So, remember when we

went back to being a a chef and the

three ingredients concept, these are

your three ingredients, right? The

prompt that you get to provide as the

instructions, the knowledge that you

provide as the external knowledge base

and the tools that we can connect to it

as well. And we can select the model

here. So, I think I want a nice and

snappy response time because this is

going to be on WhatsApp and customerf

facing. So I'll go to GPT4 mini. So it's

nice and quick. Now we can just put a

test test test in there or you are a

help. Just put that in there for now as

the prompt. The knowledge base we can

turn this on. We can create a new

knowledge base. Call

this. We're going to click here and

we're going to upload that same file

that we used on voice flow. The same

document that will be available in the

resources for this uh for this

particular guide. Give that a second to

process. Once this goes green, we're

good to upload it. And you can add

multiple files in here. We allowed five

files at a time, but you can add dozens

and dozens of files. So, you have a

massive knowledge base to work

with. And just like that, we have

connected our knowledge base. And the

cool thing about Agent is because we're

built on the assistance API, this is

actually an independent knowledge base.

So, you can create a knowledge base and

connect it to multiple different agents.

The the knowledge is not restricted to

the agent that you build it within. So

I can go and create a new agent and

connect this exact same knowledge base.

And I have all of these other ones here.

And then when we go into the tools

section, we are going to have two tools

for this. Well, the knowledge base, if

we go back to our Figma here

technically the knowledge base is a form

of tool that the agent is using. But on

platforms like the assistance API and

and many platforms, you'll see knowledge

treated as its own thing, but

essentially it is just another tool that

the agent is using at the right time

when it needs to pull in knowledge to

answer questions. So, OpenAI separates

it out into its own thing here. And so

do we because we built on top of it. So

we do have three different tools but

knowledge is its own tool that gets set

up through this knowledgebased um

connection that we just made before.

Then we have the tools and here we have

our instant quote from relevance and we

have our capture lead information. So we

know the process of going on to

relevance. So we can just go create a

new tool here and this is going to show

you a schema. Remember back to when we

talked about schemas it explains to the

agent how to use the tool. So to add a

tool to this agent we need to add a

schema to it. And thankfully our buddies

at relevance AI provide a very very easy

way to create schemas to import into

agents like on aentive. So here I can

grab that same cost estimate tool for

the instant quotation for cleaning

services that we've used previously.

Again this will be linked. You can just

clone this if you haven't got it

already. I will provide a link for you

to clone this into your relevant account

that will be in the resources for this

video. And it's just like the previous

tutorial that we did where it's got

property type square footage and an LLM

step here to calculate it. It's going to

spit that back and we're going to turn

that into a nice response uh with our

agent on Agent. We can make sure that

we've saved this. So, the cool thing

here is that in order to get this

connected to uh Agent and our agent over

there, we can just go to custom actions

here on the tools page. As you can see

here, it's mainly intended for use with

OpenAI's custom GPTs, which you can get

access through chat GPT. And I highly

recommend you do check out the OpenAI

GPTs because it's a super simple way to

spin up your own agents um on the chat

GPT site. And so, we can select our tool

here and we can get a schema for it. But

what I've just realized is that I

actually do have an air table lead

capture tool here that I've already

created on relevance. And it's actually

going to be easier for us to set it up

here on relevance than to have to do it

all separately. So let's just quickly

set that up. Now if we go air table

let's just get a simple one that

captures the name and phone. I'll

provide the template for this tool so

you can clone it in. But basically it

takes an input of the name of the lead

and the email address of the lead and

the phone as well. So, it's capturing

all three of these as lead information

and then it's sending it over to Air

Table which we're going to set up just

now and it's using a post request to

push that data that we collected here

and we will collect through WhatsApp

eventually and it's pushing it into the

Air Table database. So, let's get that

set up quickly. If you go to Air

Table and I'm just going to use a dummy

CRM that I use for all of these

tutorials and you guys are going to be

able to clone this if you want. In the

resources for this video, there will be

a link like this. So, if I share this um

share

publicly, you guys will get something

that looks a bit like this and this

button up here says copy base. That will

copy it into your account. So, all you

need to do to copy this air table base

is to create an air table account and

then click on this copy base and it will

copy it over. So, you can get this with

the column source preset up. It is

fairly easy to set up these fields

yourself, but I want to make it easier

for you guys. So, you can just copy

this. It'll be included in the

resources. But here we have the fields

that we're looking for. So now all we

need to do to send data into this

database through our WhatsApp based

agentive agent. So when someone provides

their details that it gets shot into

here is we need to go and see our

details for the air tableable web API.

So air tableable has their own API which

allows us to interact with our databases

like this programmatically. So all we

need to do is go up to the right hand

corner here go to builder hub and if we

go to the developer docs here and scroll

down to the web

API this is a reference for the air

table web API. So this documentation is

essentially going to tell us how to

interact with our air table

programmatically through our agents and

through voice flow and through relevance

and and through agentive as well. So any

way you want to interact with it, you

can now take this knowledge that you've

gained in this video look through this

and model what we're going to do here in

relevance. You can take that same idea

and put it into say voice flow and you

can build an air table integration

within voice flow yourself where you can

send and pull data. So these skills all

stack on top of each other and it really

centers around understanding how APIs

work and that comes down to reading

documentation as well. So in this case

if you go back to our relevance tool

here, we need to get our URL, which is

our endpoint, which we've talked about

before. This is the address that we are

sending the request to and agentive

where we're building the agent. It's

going to be using relevance to call air

table. It's a bit of a a roundabout way

of doing things. But to get all of this

information, the easiest way is to go

back to this documentation. And the

easiest way for us to find the

information that allows us to interact

with our own Air Table base that we're

setting up, is to come down here and

find the base that you've just cloned

into your account, which will likely be

Smith Solar CRM. Don't worry about the

name. And Air Table does a really

really good job of making this super

easy. And that we can just come here to

the leads table. So, if we go back to um

Air Table here, you see we are on the

leads table. We have these different

tabs. You can just ignore these are just

different projects that I've done on

YouTube. Um they're all kind of there in

case people are also cloning this into

their account. But we're looking at the

leads tab here. So, we go to the leads

here and we want to create records. And

then it gives us all of the information

we need here in order to create records.

So, um you can see that it's a post

request and it's got

HTTPS and all of this information. So

this is the endpoint. We want to copy

all of this all the way down to leads.

Copy this. Go back to

relevance and paste this in. Oh, maybe

that was already there. And paste that

in there. And then we need to add in two

headers. So we have authorization and

content type. Now remembering what we

learned before, you can see we have H

and this H tag means that there's a

header. And so the header is

authorization. And then the value is

going to be bearer and then our token.

This is something that tripped me up

when I was first learning this uh using

APIs. Is that you need to add this

bearer word and then a space and then

your API key. It's a weird way of doing

things. I don't really know why uh why

it's like that but sometimes when you're

doing these authorizations you need to

add in bearer space and then your API

key. So it's it's there for a reason um

is what I'm saying. And then we have the

content type being application JSON. So

we're already familiar with that. So

going back to relevance we have the

header of authorization and content type

here. Application JSON. And now we need

to add in our Air Table API key so that

we are authenticated and we have

permission to send an API request. So

they're not going to let anyone use this

details and and start sending data to

our our database, right? They need to be

authenticated and that's what API keys

do. So to get our Air Table API key, of

course, we go to Air Table. We can come

up to the top right here, go back to our

builder

hub, go to personal access

tokens, and we can create a new token.

Call this

YouTube. We can go um add the base. This

will be um Smith's Solar CRM. We can add

a scope

read write and sometimes I find it handy

to have the schemas read in there as

well. So basically what we're doing here

is saying that I give this API key that

we're creating permission to interact

with this uh this air table and I give

it permissions to do these things like

read what's in the database write to the

databases and create new things and also

to see the overall structure of the base

and the field types. So we can add that

and create the token. We get this token

head back to relevance. And again, this

template so that you can clone it into

your account is going to be on the uh on

the resources. So, if you're following

along, you should just clone it into

your account and then come down here and

make the changes as I do them. So, we

can add a make sure we have a space

after bearer and then paste our key

because if we go back to the web API

docs, we can see we have authorization

bearer space your API key content type

and then application/json. Then we have

the data as the payload. Remember, like

this is what's inside the envelope. Then

we have records and we have the fields

name, phone, and status. And then it's

provided us an example of how we would

send data into that which we don't need

to worry too much about because I've

already got this fitted in here. It can

be quite fiddly. In fact, for this I'm

actually going to add another field in

here. This is one thing about relevance

I'm not a huge fan of. This can feel

super fiddly

sometimes. So if we go

email, this will already be in the

template that I give you, by the way.

Email. Why would that delete that?

Okay, so we have the URL has been

updated. The method is post. That's

correct. We have the authentication. We

have the content type. We have the body

all set up. We've added in our fields of

name, phone, and email, which we have

name, email, and phone. So, we can give

it a spin here. If we say

Liam, there we go.

and we give it a spin. Run the

tool. We'll see if it accepts

us. Yep. And there we go. If we go back

to Air

Table, open the base

up. There we have it. Liam phone email.

So, we can take this tool and we can

take the instant quote generator. we can

put those into uh Agent and before you

know it, we're going to have our agent

ready to go. So, let's head back to

relevance here. We'll save this tool and

we'll change this to name

email, and phone. Um, and just quickly

before we do that integration, this is

when the description comes into play

here. Remember those natural language

descriptions of what the tool does, what

each of the parameters and inputs are.

It's really important to get these right

in relevance, and I see a lot of people

skipping over this step, but this is

what's going to be put into that schema

right? So when relevance generates a

schema for us, that onepage manual on

how to use this tool and use the API in

order to interact with this

functionality when we give it into

agentive, it's going to be reading over

everything in there. And it's going to

be those little descriptions around what

the tool does and what it's supposed to

take in. And and these parts here in

relevance is where we get to set that

up. A proper description is needed

before we do this integration. So this

tool captures lead information, stores

in Air Table CRM, requires lead's name

phone, and email. Name, phone, and

email. The name is name of the lead.

Yep. Email, email address of the lead

and phone is the phone number of the

lead. So that's all good there and ready

to

integrate. Might even do a quick check

on the sparkly cost estimate as

well. Yep. Type of property, square

footage of the property, and we are

ready to go. So now we can click on the

custom action step here. Scroll down and

click on both of these. Bam. Bam.

Scroll down. We're going to change this

to custom orth. We're going to generate

an API

key. There we go. And we're going to

generate our open API, not open AI, open

API. It's essentially a type of API and

a way of describing how the API works.

And it gives us all of this information

here. I will actually expand it out so

you guys can see at least some of it.

It's probably easier over on agentive

actually. And then we head back to

agentive.

What we can do is paste in the

schema. And now if we scroll through

this quickly, I just want you to see

what a schema looks like under the hood

because we have some important parts uh

that's going to really connect the dots

for you after everything that we've

learned in this video. So I'll zoom in a

bit here. Um we have the title of the

tool. So we have a few key things in

here that we can break down. Basically

the paths. We have two paths in here.

This is one of them and this is the

other. These represent the two tools

that we are integrating. You can see one

here is the operation ID is basically

the name of the tool and that is taken

from relevance directly the air table

lead capture and the summary here is the

actual name or the title of the tool

that we had in relevance. This is just a

a version where they put in um

underscores to connect the uh the gaps

and the description here you can see

it's the same as a description that we

set up over I don't want to go back on

there but that was a description that we

put under the name to describe what the

tool does and then as for the inputs

relevance has made it a little bit more

complicated by putting a schema in here.

Um, so we'll cover that in a second, but

basically here's the second tool.

Sparkly cost estimate. This tool does

this. This about estimating the cost of

an apartment. Then down here we have the

schemas for the inputs. So we have

things like the name. This is for the

lead capture tool. The name um this is

one of the fields. It's going to be in

type string and it's required. We have

the email which is type string which is

required. And we have the email which is

a description

here. And of course you can see all the

descriptions that we put in on relevance

showing up here. then the phone number

of the lead, the email of the lead, etc.

And here it's specifying how the AI

agent should be sending inputs into

that. So that's probably the most

difficult technical part of this whole

video, but I did want to give you a bit

of context on how that kind of fits

together. This is quite a complex

schema. Relevance puts it together in a

little bit more complex way um by using

these uh these schemas for the inputs

down here. But long story short, if we

then go to the add or button, we need to

set up our authentication, which we can

do by coming back to relevance and

copying this. We go back to here, paste

this in. We go custom orth and we go

authorize a

orization with a

zed. Oh, I need to create the tool.

Sorry. So, we can just click create

tool. So, the tool has been created

successfully. And there we go. We have

both of the tools added in because we

did them both in one bundle on

relevance. And then if we go edit off

we can then put in API keys for both of

these so that we have permission to use

them. And we can do the same for this

one. And there we go. Now we have our

knowledge set up, our two tools set up

and you can see that we're pretty darn

close to completing this build. We have

all of these three done. Might as well

make them green for the sake of it. And

now the only thing left to do is to

write a prompt that connects this all

together. And that's really the glue

that holds it together. My go-to method

of rapidly creating prompts for AI

agents is using a relevance tool. Um

perfect that I've created, and I I said

I'd give this to you guys for free as

well. That's going to be included in the

resources. But if I go to use

here, it's a prompt writer that includes

all of the information from how we do

prompting at Morningside, which is based

on research and includes all the key

things like RO, task, specifics

context, um, explaining how to use the

tools that it's been provided as well.

So, I'm just going to fill this out

quickly here and then get a prompt. And

you guys can steal my prompt from the

resources or you can use this as well to

create your own. But, it's a pretty good

exercise because you can see here we

have the agent name being

Um, and what I like to do here is, so

this is just a quick rundown of what the

agent does, where it is being deployed

and

why. So you can pause the video and look

at that there. But just a bit of context

on what the agent does, where it is

being deployed, and why. Conversions

context, we can say

knowledge context. We can say with a

contain. Then we get to the tools

available section and we can

say and then we have the other tool

which is uh

And then for the ideal input and output

examples, I'm just going to say none to

provide friendly helpful

assistant. And so just like that, in

maybe a few minutes, I've typed in all

of this information about the agent and

what it does. And now I can just click

run tool here. And it's going to take

all of this information, run it through

the prompt that I've written that bakes

in the best prompting practices for AI

agents from my experience and from the

projects that we do at Morningside AI

and also all the research that we've

used to make those prompting practices.

And it's going to spit us out an AI

agent prompt that we can throw straight

into Agent and it'll just glue

everything that we've done together

tell it who it is and what it's trying

to do, tell it how it's supposed to use

the knowledge base, and tell it how and

when it's supposed to use those tools in

order to reach its objective of

capturing those leads for us via

WhatsApp. And there we go. So, if we

scroll down, we can see it's spit out

this entire prompt. I'm going to change

it to the raw text so we get all this

markdown formatting included. We can

view all here. I'm going to copy it all

and we're going to take it over to the

here and paste this in. And there we go.

Act as corner cleaning WhatsApp support

and lead generation agent. Engage with

potential customers on WhatsApp to

provide potential information about our

cleaning services. Answer FAQs. answer

off instant quotes

ra when pricing inquiries arise use the

instant quote generator tool tools you

have this tool and this tool examples

I'm just going to cut that out for now

and then notes ra so that should be all

good we can start to give this a spin

here I am going to zoom out a bit right

all right so I'm just going to publish

this and make sure that everything is

baked in the second and now we can chat

to it here um hey how's it

going actually slide this across

Um, I want to know where you guys are

located. There you go. Connor's cleaning

is located at XYZ. So, it's obviously

using the knowledge base correctly and

say, "What what services do you

provide?" And we're not asking about

pricing. So, it shouldn't go for the

quote or it might try to do it at the

end. Yes. So, see, it's asking if you

have any specific requirements, need a

quote, just let me know. Yeah, sure. I'd

like like a quote. Boom. I need the

property type and square footage. It's a

house and it's 1,00 square ft. So now

the agent is trying to trigger that tool

by taking the house and taking the 1,00

and then putting them into the relevance

tool based off what the schema has told

it how to use the API. It's going to go

grab that from relevance, send it back

to us and there we go. Here are the

quotes for you. Ra, if you're interested

in any specific further service and need

assistant, just let me know. I can also

help you with booking. Now here I would

probably change the prompt and make it a

bit more forceful and say send me like

like let's go to the next step right

now. But for now it's good enough. Um we

can say uh sure I'd like to book a deep

clean. Now it should ask me for my lead

information. Okay. Huge

Jackman is the

name is the phone and huge Jack Jackman

is email.

We should be able to see if we go back

to our handy dandy air table

here. Huge popping up here.

Oh, bang. And huge Jackman is in the CRM

here. It does say that it's booked. I

would play around with the prompt a

little bit more to be like, hey, look

this is just setting up the next step

for someone to call them and book in the

service. But you can also do appointment

setting through agentive as well. Again

like I said, it's a little bit more

advanced than what we want to do here.

But as you can see, this is a very

different way of approaching building

agents because you tell it, you

basically provide all of the ingredients

and you use that kind of chef's

approach. The knowledge and the tools

and you connect it all up and you make

sure the tools have well described

schemas so they know how to use it and

they know when to trigger them. The

knowledge base has been included in the

prompt and also the tools as well have

been included in the prompt um telling

it how and when to use it. It's really a

much faster way of building agents from

the highle prompting and then people are

just asking and having sort of a free

flowing conversation with it. Okay. And

just quickly before we go to the step of

putting it onto WhatsApp which won't

take long at all. I do want to show you

how you can debug and when you're

working in agentive um it's helpful to

know when tools are being triggered and

why. So for example, if we go into the

transcripts here and we look at this big

transcript here with 14 messages that we

just had. Hey, how's it going? Ra. We

can see here it's using the tools and we

can hover over it and we can see it's

calling the tool with the URL. It's a

post method and we can see the data

here. I'll just zoom in on that. The

property type and the square footage

that was sent away to relevance are

here. So if you're having issues with

your tools or it's giving weird

responses, um you can either come in

here to the transcripts after the fact.

So say maybe this is on WhatsApp um and

something's going wrong or customers are

getting upset. You can come into the

transcripts here and pick through and

see what's going wrong with the tools.

And just like down here as well, the

lead capture, we can see the name

email, and phone were all put into this

request and sent away to relevance AI.

And then onto Air Table as a second

step. Um, and you can also see the

output as well. So the output of the

tool is all in here. It's basically just

giving us a confirmation back from Air

Table that, yep, everything went well.

And up here, you can see the

output as uh the response with the deep

cleaning estimates and stuff like that.

You can see it a lot more easily if we

say, "Hey, can I get a quote?

Okay. And if you give this a second once

it's finished generating agentive will

then pop up this show usage and bang

there in the editor here. You can then

debug. Okay. How many tokens are being

used? How much is this costing? What was

the model? Um etc. And then you can see

the tools input here. Apartment 500 ft

etc. And the output as well. So it's

really easy to debug those tools while

you're in Agent. Let's make sure that

we've published

this. I'm going to publish it again. In

Agent, we do have version history. So

if you do publish it and you want to

roll back or look at how you had it set

up previously, you can now see that I've

got two versions, V1 here, and I just

took away this little full stop here and

you can see that that's I've changed the

prompt. So you can update it over time.

You can make edits within Agentive here

and test test test. And then when you're

ready to push that to production and

basically if we had this on a WhatsApp

agent and say I published this

connected to WhatsApp and it was working

and then I looked through the the

transcripts and something wasn't quite

how I liked it, I could come in here and

make edits and then test test and then

when I was ready to publish it, I click

publish and then it's going to push

those live to the agent. So you're not

going to mess things up by playing

around with things on here. So the final

step is of course to deploy it to

WhatsApp.

Go to the deploy tab here. You can then

click connect

WhatsApp. I'm going to click

continue, get started. You will of

course need a Facebook business manager

to set up this integration fully. That's

free with every Facebook account. So, if

you haven't got one already, I'll leave

a link in the description so that you

can set it up. Takes a few clicks. Then

you will see this page here. And you can

select the business manager you've

created. In this case, I'll be using

this testing one. And then you'll be

able to set up a new WhatsApp business

account, which I can click here. I'll go

next. set up a business account

name. And then this is the display name

for the business. And we're going to

call this a a retail business. Now, you

need to provide the phone number that

you want to connect your agent to. Um

unfortunately, you can't have your own

personal WhatsApp number and also have a

business account running through it. So

you need to either buy another SIM card

or borrow a friend's number who doesn't

have WhatsApp, etc. In this case, I'll

be using a spare number that I have.

Then, they're going to send you a

verification code to your number, which

you have to enter in. And then you

should see the screen once you've

successfully passed that verification.

So when we continue, it's verifying our

information for a second. And now our

agent is connected to that phone number

and we're ready to give it a test. So if

we go finish here, there's one more

thing that we need to do on Agentive

which is to click this. Yours may say

not registered. Don't worry, you can

just click this check box here and click

confirm. Give it a second to connect.

Now we've successfully connected our

agent to that WhatsApp number. Now thing

here is this interval. If you're not

sure what the interval is, you can read

this tool tip here. And if you're done

with the deployment and you want to

remove it from that number, you can

always come back and click deactivate

deployment here. But all that's left to

do now is to test our functionality.

Right. So I have it connected to my

phone here. So I'm just going to show

you a little bit of a on screen here of

me creating this contact and having a

message with it. So the number that I

set up, I can create a new contact and

then I can send them a message.

Hey, and you can see on screen here it

says this is the business using a secure

service from Meta. So, this means this

is a business account um as we've

connected it through our WhatsApp uh

business profile that we set up

before. And there we go. We get a

message back. Hello, thank you for

sharing your information. How can I

assist you today? Um if you have any

questions about cleaning service or need

a quote, feel free to let me know. So, I

can say um yes, a quote. Let's ask a

question to the knowledge base. Where

are you

based? There we go. We are based in the

greater Boston area. It's giving me the

uh the correct location there. So, we

can go for the lead capture now. So, if

we say um I'd like a

quote. So, property type uh it's a house

that is

about 800 square

ft. There we go. We're getting the uh

estimations and our quote back. Um, it's

asking if we're interested in any of

these services. I'd say yes, I'd like

the deep cleaning

please. Now, it should ask me for my

contact

information. There we go. So, Liam, I

mean, Liam at mail.

Cool. And then we should see it appear

over here on our Smith Solar

CRM. And boom, there it is. So, we've

got everything done. That is just one

run through of using this WhatsApp

agent. But as you can see, uh the the

messages don't come back instantly. So

it it feels like it is like it's

actually could be a real human there

applying and it's giving just clear

information right through WhatsApp.

Imagine you are reaching out to maybe

book an accommodation or you're reaching

out to a a cleaning service like this or

you're reaching out to any kind of

business and you want some real

information directly from what feels

like a person. And then you also have

the functions of getting a real quote of

I mean a great use case for this kind of

thing is like barbers. Like I say, if

you you message a barber on WhatsApp

maybe you're in in Europe somewhere

you're in South America or you're in

Central America or and and you want to

go to a barber and this is a common

issue that I've run into when I'm

traveling. It's like, I want to message

this barber, but I might not speak the

language that well. And then if you

message them in English, it will be able

to handle that in in English as well as

in Spanish or in Portuguese or wherever

you are. So, this kind of functionality

built through WhatsApp is a really

really great use case for you guys to

pick up, which is why I wanted to teach

you guys it. And we can also go back to

agentive here. And if we go to our

transcripts for this agent, we can see

the one for today is here. So 12

messages. You can go through the entire

transcript and you can see it's calling

the quote tool here. We see all the

information that went in and out of it.

And then we see the air table lead

capture information as well. Input and

output. So that is how you use a genty

my software for building these WhatsApp

based and also other deployments as

well. So if we go to studio and we go to

deploy, we have Instagram as well. So

via our mini chat template. You can hook

into Instagram and do appointment

settings and things on Instagram. You

can go through Messenger if you want to

run some Facebook lead ads to Messenger

through voice flow as well. Telegram

Discord, we have integrations with

everything you need as well. So that's

the end of this build. I hope you

enjoyed and uh this is a super handy use

case um and and deployment really for

your AI

agents. So now that you understand how

AI agents works and can build them for

yourself, let's talk about the most

important part of this, which is

actually making money with these skills.

But first, we need to destroy a huge

misconception and that you don't need to

build the next chat GBT or create some

revolutionary AI startup in order to

make money in the AI space. The real

opportunity is much much simpler. It's

just helping businesses to understand

and implement AI. This is how I

monetized my AI agent skills and it has

been the most explosive growth I've ever

experienced in my career. And the good

news is, if you've made it this far in

the video, you are so much closer to

being able to tap into this starving

market for AI services than you think.

But don't take my word for it. I'm just

some guy on the internet after all.

Maybe you should listen to some of the

world's most famous businessmen saying

that this is the opportunity to get into

right now. If I was 25 years old today

in 2024, what would I do? What's a good

sector to get involved in? What business

would I get involved in? I think

everything is looking at AI now in a

different way. And I think AI growth is

going to be exponential. So, anything to

do with AI now, what could that be? In

the simplest form is helping people use

the technology. there's going to be a

massive amount of people wanting to use

it that don't know how to and they're

willing to pay to solve that pain point.

So, is that consulting? Not really. It's

implementation and execution. And so

helping a business do that transfer into

a world where they're controlling their

data and getting information from it.

Now, the majority of businesses in

America, for example, are between 5 and

500 employees. So, they're small

businesses. They create 62% of the jobs.

They want to use AI. you should help

them solve for that and they'll pay you.

Even another shark, Mark Cuban, is

saying the exact same thing that the

biggest opportunity right now is helping

these small to mediumsiz businesses who

don't understand AI yet, but desperately

need it to keep up. And they're

absolutely right. If we look at the

data, it's pretty obvious. According to

recent data, there's 1.7 million

businesses in the US alone that are

making between $500,000 and $10 million

per year. These are small businesses

which, as Kevin Oer says, make up 62% of

the jobs in the USA. They create 62% of

the jobs. They want to use AI. You

should help them solve for that and

they'll pay you. These businesses know

they need AI to stay competitive, but

they don't have the time to learn it

themselves. And there's basically no one

there to help them. All of the big

consulting firms are looking at other

big businesses and just leaving these

smaller businesses completely ignored

but they still make lots of money and

they still have a lot of money to invest

in these kinds of services. Basically

all small businesses are starving for

some kind of AI services, either

education services to help them

understand what AI is in the first place

and why they might need it. There's the

huge need for consulting services where

you help them to identify where AI can

help with them most in their particular

business. And of course, there's

implementation services where you help

them to build and maintain the AI

systems like the AI agents we've just

built. And right now, based on the data

collected in my community, and we are

the largest AI business community on the

planet right now, for every person or

agency that is currently offering AI

services, there are over 1,100

businesses in the USA alone that need

help. So, that's a 1 to 1,100 ratio

which means this is a completely

untapped market. And that's where people

like you and I come in, helping these

hardworking small business owners to

understand AI and implement it so that

they have a chance to keep up. And

that's really what drives me and the

team at Morningside because our company

vision is a world where the benefits of

generative AI are distributed as fairly

as possible and they make it to people

like me and you and the small business

owners rather than just all going to

these giants at the top. And this whole

concept of selling services around an

emerging technology is nothing new. And

we saw the exact same pattern when the

internet first came out. Companies that

helped businesses to adapt to the web

and sort of get online made fortunes.

You know, agency.com, Razerfish, etc.

And I spotted this opportunity within

the AI space in 2023 when it wasn't

anywhere near as clear as it was now. No

one really knew how to make money out of

this stuff. And then I started Morning

Side AI. And since then, we've generated

over $5 million in selling these kinds

of AI services of education, consulting

and implementation. And we're literally

still only just getting started. And the

best part out of all of this is that as

we've proved in this video already, you

don't need to be a technical genius to

understand AI and even to build AI

agents. You just need to be one step

ahead of the businesses that you're

going to be helping. So, let me show you

the three specific ways that you can

start making money with your AI agent

skills. So, as I said, there's basically

three types of services that you can

provide to businesses in order to

monetize your skills. Firstly, there's

education, and this is teaching

businesses about AI, running workshops

and doing presentations, training their

teams, and creating courses for them to

watch. Businesses are desperate for

someone who can explain what this stuff

is in simple terms and more importantly

what it can do for them. After watching

this video and probably my other huge

video that I did on AI agents, which

I'll link down below, um you will know

more than enough to start educating

businesses on AI and AI agents. Secondly

is consulting. And this is where you

analyze a business's operations and you

show them where AI can help them save

time or make more money. You're

essentially being their AI strategist.

For example, you could go into a

business and then recommend something

like the sales co-pilot system that we

just made in order to help their

struggling sales department. And third

is implementation. So this is where you

actually build and deploy AI solutions

for businesses. Or better yet, like my

agency, you can do all three of these

but it did take us 2 years to get here.

So there is really no rush. You just

pick where you want to enter and work

your way up to doing more and more if it

makes sense. Believe it or not, there

are people with only a few months

experience in the AI space selling all

of these right now. And the demand from

businesses is increasing insanely fast

right now. I we're seeing this at

Morningside. Just so many more

businesses reaching out. But here's the

thing. You have one small problem, and

that's that you don't quite know enough

to start moving on this. You are close

but you're not quite there. The way to

make money in the AI space or with any

services really is to create a knowledge

gap between yourself and the people that

you're helping. Your knowledge gap is

your money maker, and businesses will

pay you in proportion to how much more

you know about AI agents and their

business applications than they do. Now

while this video has taught you a lot

your knowledge gap is still small. But

we can fix

that. So, let me break down exactly what

you need to do next in order to extend

your knowledge gap to the point where

you can start making money. We can call

this video as step one. So, as long as

you've taken notes and followed all the

tutorials and built the agents alongside

me, you're already ahead of most people

who have no idea about what agents are

how they work, or how to build them. So

it's a big step forward with this video.

But step two is building even more

experience building AI agents. So you

are more familiar with the platforms and

better understand the different ways

that they can be used to deliver

different kinds of AI agent use cases or

even just AI tools in general. I've only

really given you a taster here, but I

tried to make it as as diverse as

possible as you could probably tell. In

order to do this second step of

extending your knowledge gap further and

building more experience, you can go to

my free course on school where you'll be

able to build another 5 to 10 agents

following the tutorials that are in

there for you. So the link to join my

free school will be in the description.

So, if you blast through all those

tutorials in there, this is going to

further expand your knowledge gap. And

remember that the more that you know

compared to the businesses that you're

trying to help, the more they're going

to pay you. So, step two is building a

few more agents out and getting a bit

more experience on the tools, seeing

different use cases, etc. And once

you've done that, you'll have what I

call foundational knowledge. So, you

understand the core AI concepts that

we've been through in this video. You

can build basic solutions on these

platforms. You know what's possible for

businesses with agents right now. And

then comes the big decision. Do you want

to go deeper technically on this

building side of getting your hands

dirty or do you want to start monetizing

what you already know? As we've covered

the building and implementing of the AI

systems is only one of the services that

you can sell. So naturally, the

technical skills needed in order to make

money in the implementation services.

Actually building these systems and

businesses is much more greater than

just having a foundation. But with a

good foundation, you're basically ready

to start having a crack at the other two

services of AI education and AI

consulting. So, the decision of what to

do next comes down to really knowing who

you are and what you are really

interested in. And this sounds all woo

woo and like, oh, you got to know

yourself and stuff, but this is I mean

it very very seriously in that if I use

myself an example, I've always loved

making things, right? I used to build

block houses with kids. I used to like

brew beer with my grandpa. I've always

loved tinkering with engines. So when I

hit this foundational level that you

guys will be at after completing those

extra builds in the free course, I kind

of naturally just dove deeper into the

technical side into building more stuff.

I I kept building more and more complex

AI systems and building upon those

skills that I've I've built already

which led me ultimately to starting

Morningside AI where our first service

was building AI solutions and systems

for clients. But here's the thing, of

course, a lot of people aren't like me.

They don't get as much of a buzz out of

building things. many of you are going

to be much better or enjoy more the

teaching aspect or working with people

and doing the consulting aspect rather

than building stuff. So in these cases

using the foundational knowledge that

you're going to build up to sell AI

education to businesses or AI consulting

makes a lot more sense. Goes back to the

whole Einstein thing about like judging

a fish on its ability to climb a tree.

If to you the building is like a tree

and you feel like a fish and it's not a

really good fit, then there's better

stuff that you can do and you can find a

way to make money in the AI space that

leans more into your strengths. like in

the case of a fish would be swimming

right? So, by being honest with yourself

and saying, "Hey, look, that's not

really me. Yeah, sure, I did get it

done. I know how that works now, but I

don't feel any kind of attraction to

doing more of that." While you may see

that as a negative and saying like, "Oh

I don't have what it takes." It's

actually can be very empowering if you

say, "Bang, I'm stopping it here. I'm

stopping the learning. I'm stopping the

procrastination. Now, I'm going straight

into actually monetizing." It's

basically putting a stop on when you do

this learning big long phase and saying

"No, action starts now. I'm not I'm

never going to get there, but with this

base, I can do a lot and I'm going to

start taking action with it and making

money with it today. So, this

self-reflection is really what prevents

you from getting stuck in an endless

learning phase of procrastination when

you could be out there making money. So

in summary, the two routes you have and

the two options you have from here are

if you love building and you kind of

naturally feel like you want to learn

more like like myself when I was at your

stage, then just keep going. go and

watch the free course tutorials on my

school and then start going and building

your own projects and ones for friends

and family and whatever you you sort of

pulled towards naturally and within two

to three months you'll have enough

skills and experience to actually start

selling implementation properly. But on

the other hand, if you haven't fallen in

love with the building aspect, then it's

probably best that you just go go into

the free course, smash out the rest of

those tutorials and finish your

foundation and then just get started on

monetizing your skills either through

selling AI education or through selling

AI consulting.

So once you're clear on what kind of AI

services you want to sell, getting your

first few clients is actually pretty

straightforward. People try to over

complicate it, but there's really just

two main ways that I'd recommend you do

this based off all the success I've seen

over thousand thousands of people across

my free and paid communities. The first

and by far the easiest method is through

your warm connections or warm contacts.

This means reaching out to people that

you already have some kind of

relationship with, whether it's friends

or family or kind of acquaintances or

even friends of friends that you've met

once kind of thing. All of these people

count as warm connections. So instead of

trying to convince complete strangers to

trust you with your business, you can

start with people that you already know

or have some previous relationship with

and therefore have an increased level of

trust with you through your

relationship. And I've covered this

many, many times on the channel before.

So on the school post for this video, I

will add in my complete guides for warm

outreach, including resources directly

from my AAA accelerator program. The

second way is using what I call the

community content flywheel. So this is

how you can build long-term momentum

beyond just warm outreach. So here's how

it works. You join the free school

community. you start making content

about what you're learning at each

stage. This could be through YouTube

tutorials, which I mean that worked for

me. LinkedIn post is another one um or

whatever platform you really prefer to

create content on. But here's the key.

You share that content back into the

community. So with over 120,000 members

by posting it into the community, you

get an instant audience and people who

are really interested in the stuff that

you're talking about. So, a perfect

example of this is a guy called Rory

Ridges, a a young guy from the UK who

joined my free community and basically

followed this exact process that I've

told you in this video so far. So, he

took my free course, he learned all the

basics, built his foundation, then he

started posting simple tutorials on

relevance AI, which you've used in this

video already, and he literally just

started sharing what he'd learned from

my videos and making other videos about

it. And at the start, he was literally

just sharing what he'd learned from my

videos. So, he'd watch a video, then go

and kind of make his own video on the

same sort of topic. And every time he

made a tutorial, he'd then share it into

the community and the community would

watch it. They'd give him feedback, go

and subscribe to his channel. This not

only helped him grow faster on YouTube

but it also started to position himself

as an expert in the community. And he's

also building his authority in the AI

agency space by getting more momentum on

YouTube. Now, his YouTube channel brings

him in enough leads to support his

growing agency. And I've just seen him

recently in the community saying he's

hiring. So, that's usually a bloody good

sign that he's making some good money

off the back of it. He basically started

the same flywheel that took me from zero

to where I am now. Over $5 million in

revenue generated across all my

businesses and $450,000 plus subscribers

in just two years. And so the community

gives you an audience and the content

gives you credibility and together this

method brings you clients. In the

resources for this video on school, I

will leave links to my complete guide

for creating content to generate leads

just like Rory and I have done

successfully. And of course, I'll

include a link to Rory's channel in the

resources in the school community. Now

the really important thing to notice

with both of these methods we've just

talked about, the warm outreach and the

uh community content flywheel is that

both of these methods start with giving

value first. Whether it's helping your

warm connections to understand AI or

sharing your knowledge through content

you have to give before you start to

get. Now, I know all of this businessy

stuff may feel a little bit overwhelming

or out of reach for some of you, but

you'll seriously be amazed at what baby

steps add up to in the AI space. you've

already taken the first step by watching

this and following through to the end of

the video. So, congratulations on that

and I seriously seriously want to give

you a pat on the back and you should

give one to yourself. But all you need

to do from now is to keep this momentum

going. The next step for all of you is

pretty damn clear. You need to jump in

the free community. That's by far the

best place if you're moderately

interested in doing any of this stuff.

My community is the number one place to

go. It is 100% free to get into. And

once you're in there, drop an

introduction post saying who you are

what you're about, why you want to do

this. Then start working your way

through my free course material. I've

poured everything I've learned about AI

and AI business into videos like these.

And on the free course, they're all

there in a nice sequence for you to work

through on school. And each time you

complete a video and you complete the

tutorial, you can click the little check

box so that you can keep stacking those

small wins and those baby steps towards

AI literacy and succeeding in the space.

All of the resources mentioned in the

selling part of this video will be on

the school post for this video. So you

go into school, you go to the YouTube

resources tab, and then this video will

be right there. So don't forget to check

all those out. And of course, all of the

resources for the tutorials, if you

haven't already done them, are included

on those posts as well. And finally, if

you made it this far, could you please

do me a favor, leave a like on the

video, and drop a comment down below.

Let me know what you like the most, what

you want to see more of. Click the share

button, send it to your friends and

family and loved ones so they can start

learning these skills, too. All of these

actions really help my video to reach

more people in the YouTube algorithm.

And if you subscribe to the channel

you'll be able to see a lot more content

like this helping you to understand AI

and more so how to build businesses

around this incredible opportunity. And

if you're still hungry for more and you

want to watch my complete guide to

building an AI business, that's going to

be linked up here. But that is all for

the video. I'm so excited for you to get

cracking on this. I sincerely hope

you've got something out of this because

I put a lot of work into it as did my

team. So I'm just really really wishing

you all all the all all the best. I'll

see you in the next one.