AI Agents Fundamentals In 21 Minutes

I learned about AI agents for you so

here's the cliffnotes version to save

you weeks of me learning about this

there's not actually one course that

just fully nicely covers everything so I

did three courses wrote a bunch of

papers and watch a lot of YouTube videos

as well and of course actually made my

own agents too my notes themselves are

over 200 pages long but as per usual it

is not enough just to listen to me talk

about stuff so at the end of the video

there is a little assessment which if

you can answer these questions then

congratulations you are now educated

about AI agents now without further Ado

let's get going a portion of this video

is sponsored by HubSpot here's the

outline first we're going to talk about

what even are AI agents it is such a

hyped up term now then we'll do a crash

course on specifically multi-agent

architectures it's really interesting

developing field to make this actually

all practical I'm going to then show you

how to create an AI agent workflow which

does not require any code I was honestly

so shocked by how powerful and easy to

use as well these workflows are then

finally for those of you who are

interested in getting into the field or

even building your own AI agents for

your businesses I will leave you with a

piece of advice that when I heard it I

was like holy so stay tuned for

that at the end all right so let's first

Define agents okay so believe it or not

one of the most difficult things from

this entire Deep dive into AI agents for

me was just the actual definition of an

AI agent probably because it's just such

a new field and people are still trying

to figure out what even it is and like

how it works works so before watching

this video if you were also confused I

promise you it is not you let me walk

you through this the easiest way to

First Define ai agents is the given

example of what is not an AI agent what

is definitely not an AI agent is if you

just ask an AI to do something for you

otherwise known as one-hot prompting by

the way if you're interested in leveling

up your prompt engineering skills I did

a video over here where I distilled down

Google's 9-hour prompt engineering

course into only 20 minutes so check it

out anyways okay so what is definitely

not an AI agent is if you're just asking

AI to do something directly for example

if you just go to chat gbt and write

please write out an essay on topic X

from start to finish in one go you'll

still get a response and it'll still be

like coherent and on topic but it'll

probably also be quite vague and

probably not what you were looking for

on the other hand if you use an agentic

workflow that will significantly improve

your results and what that would look

like is to break down that overarching

task into different steps like first

maybe writing an outline for the topic

consider if you may need to do some web

research then you might write your first

draft consider what part of that draft

may need more revision or more research

revise your Draft before ultimately

coming up with the essay a non- agentic

workflow is just from start to finish

and you're done while an agentic

workflow is more a circular iterative

process you think and you do research

come up with an output and then you

revise that and then you think and you

do some more research come up with an

output and you keep doing that until you

get to your final result non agentic

workflow straight up and down a gentic

workflow

circular okay so now let's add in a

little bit of complexity you got your

non- agentic workflow then you got your

agentic workflow then you have a third

level which is a truly autonomous AI

agent this is when an AI can completely

independently figure out the exact steps

which tools to use go through that

circular process of revising things by

itself to finally come up with an output

this is the level that we want our AI

agents to become but currently as of the

time of this filming at least we are not

quite there yet we're still focusing on

this second level of agentic workflows

where there's certain agentic components

to it but it's not fully autonomous yet

but honestly with speeda AI is

developing who knows maybe in like 2

months that's going to happen we'll see

Jarvis you there that's your

Serv according to anging who's kind of

like the Superstar of the AI World there

are four massivly accepted agentic

design patterns the first and simplest

pattern is called reflection where

you're simply asking an AI to more

carefully look through its own results

for example you might ask an AI to

please write the code in order to

complete you know a specific task and

the AI is going to Output some code but

you're not going to stop there you're

going to ask the AI to please now check

the code carefully for correctness style

and efficiency and give constructive

criticism for how to improve it the AI

could look over its own code and then

maybe find out that it made it a mistake

on line five and in which case they can

actually fix that line of code and

continue improving its own output you're

sort of helping that ai go through that

circular agentic process to improve its

output a very simple extension of this

is instead of you being the one to help

the AI figure this out you can actually

create another Ai and have the other AI

prompt the original AI to go through its

own code and go through the reflection

process so this is called a multi-agent

framework and that's something that we

will talk about a little bit later in

the video and it's like a really really

interesting field next up is tool use by

giving an AI the ability to use tools

you can help the AI better break down

task and execute specific parts of the

task for example if you're interested in

buying a new coffee machine you can ask

Nai what is the best coffee maker

according to reviewers now if you give

your AI the ability to search the

internet like a web search tool you're

allowing it to add in the steps of

actually searching different reviews on

the internet compiling them together

before summarizing its findings which

you would get a much better result than

if you just ask it to directly come up

with an answer another powerful commonly

used tool is the code execution tool

this allows your AI to actually create

and to build build things like build out

a website or calculate things things

that involve numbers and math for

example you can ask the AI if I invest

$100 at compound 7% interest for 12

years what do I have at the end your AI

then can use this code execution tool to

come up with the answer for you there

are lots and lots of different tools

that you can equip your AI with

including object detection web

generation ability to access your emails

and your calendars to schedule events

for you tool use is a very powerful

agentic design pattern next up is

planning and reasoning this is when you

can give an AI a certain task that you

want done and it's able to figure out

what are the exact steps to accomplish

these and what are the necessary tools

that it needs in order to accomplish

these steps for example you can ask an

AI please generate an image where a girl

is reading a book and her pose is the

same as the boy in the image example.

JPEG then please describe the new image

with your voice with this agentic

framework it's able to First Look at the

image access a specific model to

determine the pose of the boy in the

image use another model to convert that

specific pose to an image of a girl and

another model to translate the image to

text and finally a text to speech model

to describe in audio what it is that the

girl is doing a girl is sitting on a bed

reading a book now finally we have

multi-agent systems this is when instead

of just having a single large language

model a single AI do a certain thing you

actually want to prompt different large

language models to have different rules

so the question you might have is like

why can't you just have one Ai and just

tell it to do everything right and the

reason for this is that AI in this sense

is actually quite similar to humans just

like if you're trying to complete a

project it's better to have a team of

humans that all have their own

specialized rules to come together to

complete the project as opposed to just

have like one person trying to juggle

and handle everything same thing for AI

there's research that shows by having

this multi-agent workflow the results of

the final product is generally better

than just asking one AI to do all of it

okay so here's a pneumonic in case you

can't remember what the four agentic

design patterns are just think about red

turtles paint murals reflection tool use

planning and multi-agents hint this will

help in the little assessment at the end

of this video okay so to make this all a

little bit more concrete anding also

showed us some tasks like some really

cool tasks that were able to be

accomplished by using these agentic

design patterns for example like with

this tool that has a agentic workflow

built into it you can take an image of

this soccer game and be able to identify

Y and count number of players on the

field you can also do stuff with video

by prompting it given a video split the

video into clips of 5 Seconds and find a

clip where the goal is being scored

display the frames associated with the

goal that is pretty cool just thinking

about the use cases you can do with so

much video and image data that is

currently untapped some other examples

of a gentic systems that have produced

really good results include AI powered

research assistants that's able to

research specific topics AI writers that

can then write down these topics coders

who can create software and personal

assistance which I will actually show

you how to build one later in the video

as we see today AI agents and agentic

workflows just like any other AI tool

has a large component of prompt

engineering it just shows that prompt

engineering really is one of the highest

Roi skills that you can learn today so

if you're interested in leveling up your

prompting skills I highly recommend that

you check out this free prompt

engineering Quickstar guide that I made

with HubSpot it includes a step-by-step

guide for creating great prompts and

also tips to get better results my

favorite part is that for all the

examples there's a flow from bad to good

to Great prompts to show how you can

improve a prompt if you're able to go

through this process and create great

prompts you would just become so much

more productive and get so much more out

of AI so if you're interested do check

it out at this link over here also

linked in description thank you so much

Hobs spa for creating this free resource

with me and for sponsoring this portion

of the

video next up I want to do a quick crash

course on multi-agent design patterns

specifically this is where the 's a lot

of focus and really cool breakthroughs

that are happening I did a couple

courses the best course that I found

specifically for this topic was one by

crew AI in collaboration with deep

learning AI this course by crew AI gives

a really good introduction to different

types of multi-agent design patterns

which I'm going to Now cover the first

building block is a single AI agent and

a single AI agent has four components it

needs to have a specific task and answer

what it's supposed to give you the model

itself and tools that it has access to a

nice little pneumonic here is tired

alpaca's mix te task answers models

tools for example you can have a travel

planner AI agent its task is to plan a

3-day trip to Tokyo on a budget the

answer that you want is a detailed itery

with locations and cost as well as hotel

bookings and any tickets the AI model

could be anthropic CLA for example

although you can switch that out for any

other models that you like as well the

tools that it needs include Google Maps

Skyscanner for figuring out what the ti

tickets are how much they cost

booking.com for Logistics and your saved

credit card informations so that you can

actually place these bookings task

answer model tools tired alpaca's mix te

okay so we have our first singular unit

of an agent and the simplest multi- aai

agent would just be have two AI agents

that work together on something each AI

agent has its own programming but

they're working together towards

something an example of this would be a

writer agent who is meant to write a

blog article and an editor agent who is

providing feedback for the writer even

say with just two agents there's a

couple interesting points here an agent

can have its own task but an agent can

also be working with another agent on a

task while having its own task as well

so there could be a lot of crisscross

that's happening and for tools agents

can have their own separate tools but a

task can also have a tool which is

really interesting you can actually

program a task to have a specific tool

so that an agent can only have access to

it for that task and if you have more

than one agent then you have a crew

hence the name crew AI now when you add

in additional agents there is even more

complexity and it becomes really really

interesting on how agents are

interacting with each other I can go on

for ages about all the different

configurations of Agents working

together and the tools that they're

using but this course does give us a

really nice kind of overview of the

different design patterns that people

have used and seem to be really helpful

the first one is the sequential pattern

this is the simplest when you just have

one One agent do something and then it

passes it on to another agent that does

something else and another agent that

does something else sort of like an

assembly line an example it has would be

AI powered document processing you can

have your first agent which extracts

text from scan documents that it passes

on to another agent who summarizes the

text then passes on to the next agent

who then extracts action items and puts

it into a summary and finally to a

fourth agent that saves the data into a

database a higher article higher AR a

higher AR h two hours later higher

article agent system would have a leader

or manager agent that supervised

multiple agents that have their own

specific task these sub agents will

complete their task and Report their

results back to the manager agent who

then compiles it all together an example

of this would be writing a report for

business decision-making you have your

manager AI agent that receives this task

and then delegates it to different sub

agents sub agent one monitors and

reports back market trends and it would

have specialized tools for looking into

these markets sub agent 2 could be

monitoring internal customer sentiment

so has access to the internal databases

to see what kind of feedback customers

are giving while sub agent 3 tracks

internal metrics across the company so

it's understanding how this specific

product is interplaying with other

products within the company now after

all these agents do their job they would

all report back to the manager agent

who's able to combine everything

together and it might actually pass this

along to another agent say like a

decision making agent who may aggregate

different insights and professionally

put it into a report and come up with a

ultimate business decision next up is

the hybrid system this combines

different sequential and hierarchical

structures together agents can

collaborate top down as well as

sequentially an example of this would be

in autonomous vehicles at the top level

you might have a AI agent that plans the

overall route and traffic strategy for

an autonomous vehicle then you have the

sub agents that handle things like

real-time Sensor Fusion collision

avoidance

and road condition analysis but it's not

enough just to aggregate this

information together and then just give

it to the top level AI because you need

to have a continuous feedback loop as

the vehicle itself is moving and the

road conditions and everything around it

internally and externally is all

changing as well you need to have lots

of different little feedback loops

between these different agents and then

communicating continuously with the top

level agent as well this design pattern

is really common in things like robotics

navigation systems and adaptive AI

systems basically like in places where

there's lots of moving Parts there are

also parallel agent Design Systems this

is when you have agents working on

different work streams independently

agents would be handling different parts

of a task simultaneously often to speed

up processing an example of this would

be like AI for large scale data analysis

this is a very common structure the very

large analysis involves different

components and agents will take chunks

of that data and process them separately

ultimately at the end merging everything

together and finally there's

asynchronous multi-agent systems this is

when agents execute tax independently

and at different times this is a system

that's proven to handle uncertain

conditions better than sequential or

parallel approaches an example of this

would be something like an AI powered

cyber security threat detection you got

agent one that's monitoring Network

traffic in real time agent two that's

monitoring suspicious usage patterns and

agent three that's just randomly

sampling and testing out different use

cases when any of these agents picked up

something anomalous they would flag it

and then other things would happen after

that this type of AC synchronous design

pattern is especially helpful for

anything that requires real-time

monitoring or self-healing systems and

finally to put them all together you can

actually have these different systems

and then link up these systems

themselves and this is called a float

this can result in really complex and

interesting processing and results but

the note to make here is that as you

increase the complexity of these systems

you're also basically increasing the

amount of chaos that's within it as well

since you don't actually have like

Direct access to these agents right like

you can provide them with feedback and

there's ways of doing that but as you

add on more and more complexity there's

more things and more moving parts that

are kind of just like interacting with

each other it's actually pretty similar

to how human companies work right the

bigger your company becomes the more

chaotic it starts becoming as well and

the more emphasis you need to place on

like hierarchies and different you know

organization structures I don't know

this for sure but if I were to bet I do

think a lot of research that people do

into systems like human systems and

companies probably also comes into play

for multi-agent AI systems too for the

rest of the course they basically go

through different implementations and

examples for these different multi- aai

agent systems so instead of going

through all of these examples I'm just

going to link in the description some of

these notebooks where you can use code

to implement these systems using crew AI

but do not worry if you're not a coder

where you're just not interested in

coding I'm actually going to now show

you a way of creating these multi- aai

agent systems completely with a no code

tool called n8n robot building sequence

activated I'm so glad we tried out our

new Android building device instead of

using that old dinosaur some of you guys

may have heard of make.com which people

also use to make these multi- aai agent

systems um but na an is actually better

for doing this specifically credit here

to David Andre's 40-minute tutorial

which is what I follow and adapted to

create my own AI assistant this is a

telegram based AI assistant that's able

to communicate with you and help you

prioritize your task by accessing your

Google calendars and it can also create

calendar events for you so you can go on

Telegram and talk to Inky bot which is

the assistant's name and say what do I

need to do today and it tells me that

today is February 5th 2025 and I have to

film this video and the time is from

12:00 p.m. until 400 p.m. in Hong Kong

and it also asked me to list what are my

other priorities for today so that it

can come up with a list of tasks and

prioritize it for me so I'm just telling

that filming is my greatest priority and

have these other things so it's able to

prioritize and put in sequence my other

tasks as as well as actually schedule

calendar events corresponding to these

specific task okay so the way that this

flow works is first you have the

telegram trigger so this is when I send

a message to Inky bot and from there

there's a switch um this is because it

can take both text and voice input so if

it's text input you would just directly

take that information and feed it into

the AI agent but if it's voice input we

first get telegram to get the file send

it to open AI to transcribe the file and

then send the text information to the AI

agent as well now the AI agent here is

the interesting part remember tired

alpacas make tea the task is taking the

user's query asking about what needs to

be done for today the answer is a

prioritized to-do list as well as

scheduled events into Google Calendar if

needed the model we're using here is

open AI GPT 40 mini but you can also

change that out for whatever other model

that you want as well like Claud Gemini

llama deep seek whatever you like and

finally it has two different tools the

first tool is the get calendar events so

it's able to read the Google calendar

and see what events there are for the

day it can also create calendar events

so when the user wants to add other

events into the list it can then go and

actually create these events on the

Google Calendar yeah and then it would

be able to communicate through telegram

with the user until it comes up with a

list that the user is happy about they

can also do things like check off the

list plan ahead look at what happened in

the past a lot of other things as well

as you can see just the single agent the

super simple work flow can already

produce really cool results so think

about adding other agents there other

functionalities it's really really cool

what you can do with this and it's

totally no code which is

crazy all right final section is on the

opportunities for AI agents I watched a

lot of YouTube videos and read a lot of

Articles mostly for this section and the

biggest takeaway that I got from this

like assuming you want to be building

something thing using AI agents

something that is useful for other

people you're building up a business is

from this why combinator video where

they say that for every SAS or software

as a service company there will be a

corresponding AI agent company let me

just like repeat that because this is

like huge guidance in terms of what to

build for every software as a service

company like all the software service

companies that we see today there will

be a corresponding AI agent version of

that so if you don't know what to build

or what to do right now and you want to

play around with a agents just literally

take a SAS company and then think about

how do I make that into an AI agent

company just ask chachu BT what are some

top SAS companies says Adobe Microsoft

Salesforce Shopify link tree canva

Squarespace and on and on and on and on

there are so many literally every

company that is a sass unicorn you could

imagine there's a vertical AI unicorn

equivalent I really think that piece of

advice is literal gold let me know in

the comments if there's a specific AI

agent that you're interested in building

or an AI agent business all right we

have come to the end of this video thank

you so much for watching through it as

promised here is a little assessment if

you can answer all these questions then

congratulations you can consider

yourself educated on AI agents let me

know in the comments what other topics

whether that's like AI topics or other

topics is fine as well that you want me

to do a deep dive into all right thank

you all so much for watching and I will

see you guys in the next video where

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