Building AI Agents that actually work (Full Course)

I think AI is confusing. There, I said it. I think there's a lot of terms,

it. I think there's a lot of terms, skills, MCPs, agent harnesses that are difficult concepts to understand. So, I

had my friend Remy come on the podcast and explain it in the most simple terms possible. In this free course on how to

possible. In this free course on how to master AI agents, he breaks down exactly what each piece is, how they connect together, and the simplest ways

beginners could start using them today.

Enjoy the episode.

>> I beg them to come on. Remy Gasill's on the pod. You've structured your company

the pod. You've structured your company where you basically have these folders and MD files that run your company. And

what I want to do today is I want you to teach people in a beginnerfriendly fashion. This is only for beginners. How

fashion. This is only for beginners. How

they could do the same thing. How they

can set up their own executive assistant, head of marketing, chief financial officer. Basically, I want you

financial officer. Basically, I want you to bas to tell us the concepts behind all this. By the end of this episode,

all this. By the end of this episode, Remy, do you think you can do that?

>> 100%, Greg. We're going to go through all the concepts that make up an AI agent. And by the end of this video, you

agent. And by the end of this video, you will know exactly how you can build up um agents to run complete departments of your life and your company within any agent platform you choose, whether it's

Claude Code, Codeex, Open Claw, Manis, all of them. All right, let's do it.

>> Sweet. So, one of the reasons why I really wanted to make this episode is because I feel like the AI landscape is moving into like stage two from chat to

agents. And most people are getting left

agents. And most people are getting left behind right now just using the chat models. And uh the founders and

models. And uh the founders and employees that are utilizing agents are like no word of a lie 10 to 20 times more productive in their day. And when

you stack that up over days, weeks, years, you're going to just be miles ahead of the competition. So, I really want to make this episode today to help bring everyone up to where the AI landscape is at the moment and to start

using agents to manage every department of your business.

So, the key thing to understand here is chat models versus agents because the word agent is thrown around lots online.

And I'm sure you've seen it, Greg, like AI agents this, agents this, use this agent for this, and it's kind of lost a lot of meaning. So, I wanted to give start by giving a really clear definition of what an agent actually is.

So, the way I think of it is a chat model is question to answer, but then an agent is goal to result. So moving from just like uh you asking AI replies then

you do the work to you giving the agent a task it planning out the task and then executing and then delivering you a result. Does that make sense?

result. Does that make sense?

>> Crystal clear. I mean the way I think about it is chat is kind of like pingpong back and forth back and forth.

>> Yeah.

>> And agent is uh you know you're giving it it's a goal. I mean the best way Yeah. You're

goal. I mean the best way Yeah. You're

giving it a goal and you're hoping that over time it gets better and closer to that goal.

>> Exactly. Yeah, that's exactly it. And I

just think that's a nice way to lay it out in your head is chat is question to answer. Agent is goal to result. So when

answer. Agent is goal to result. So when

you chat to an agent, you might give it a task like build me a website for XYZ and then it goes away, it does its work and outputs this wonderful website to

you. But it's really important to

you. But it's really important to understand what's actually happening in this step here. So inside this agent step, we have what's called the agent

loop. So you give it your prompt or task

loop. So you give it your prompt or task and it goes through these three steps here, which is observe, think, and act.

So, let's just say for example, um we're actually going to do this demo after this, but if we gave the agent a simple task like build me a minimalist portfolio site for Greg Eisenberg, it's

going to start by like you've loaded in that prompt. It's going to check if

that prompt. It's going to check if there's any files in the workspace that it can work with, like maybe you've got some information on Greg Eisenberg. Um,

and then it's going to think about what to do next. It's going to act and then it just keeps going through this loop.

So for that actual example of building the portfolio site for Greg Eisenberg, let's just say it was a blank agent, we hadn't given it any context. The first

thing is it's received this prompt to build the website. And the first thing it's going to be thinking about is, okay, well, I need to build this website about Greg. Who the hell is Greg

about Greg. Who the hell is Greg Eisenberg? So it's going to then decide

Eisenberg? So it's going to then decide to do some research into Greg Eisenberg.

It's going to research everything about Greg and then feed it back into this observe step. So then it's going to

observe step. So then it's going to think to itself, okay, so I've got this prompt to build a website. I've now got my research here, so I know exactly who Greg Eisenberg is. And then it's going

to start thinking, what is the next step? And the next step is probably to

step? And the next step is probably to write up a plan to build the website. So

it might write up that plan, feed that back in. Now it's got the research, the

back in. Now it's got the research, the prompt, the plan, and it will think, all right, what next? I should probably write the code. to write the code, feed it back in, and it just keeps going

through this loop as many times as it needs until it can conclude uh that the task is complete. And how it concludes that the task is complete is based on the parameters that you set in your prompt. So, you know, if you're giving

prompt. So, you know, if you're giving it a research task, you might say compile 10 sources and then create a report as a PowerPoint. And then once it's compiled 10 sources and built the

report as a PowerPoint, it can conclude that the task is complete and then give you the output as the user. The the

agent itself >> is made up of these four components. So

it's the LLM which is the brain behind it. So think like you know Claude Opus

it. So think like you know Claude Opus 4.6 or GPT 5.4 or Gemini 3. It's the

model. Uh, it's got the loop, which means it just keeps going until the task is done and doesn't stop after one response. So, you're going from pingpong

response. So, you're going from pingpong to like it continuing to go rather than you having to sit there babysitting it.

Uh, it connects in all your tools and then it connects in all the context. And

a platform that facilitates this process and basically facilitates this loop to happen is known as an agent harness.

And all of the popular AI agent platforms on the market that you'd be familiar with are just agent harnesses.

They're just applications where this loop is facilitated.

And I want to actually run this little prompt I prepared earlier. I want to open up codeex claw code and anti-gravity. And I'm going to show you

anti-gravity. And I'm going to show you this loop uh actually happening in action. So I've uh nicely prepared

action. So I've uh nicely prepared before the episode these three demo folders which we're going to run in. So,

I'm going to open up demo one to work in in Claude code. And the way these folders work is if you've used if you're familiar with like any of the chat models like Claude and Chatgbt, there's

a projects feature which is where um if I open it up actually, try not to get dizzy with me switching tabs so much.

But, you know, if we create a a project here, it contains all your chats in one place. It allows you to upload all your

place. It allows you to upload all your sources here, which is your context. And

then you can even add custom instructions which tells it how to behave within this project. And that's

also known as a system prompt which we're going to dive into how to do this with agents as well later. But it's a similar concept that you'd be familiar with if you've used projects before. But

instead of the project being here on the cloud, we're actually working within projects that are local in our computer.

So I've just selected this demo one for now. Then we're going to run build a

now. Then we're going to run build a minimalist portfolio site for Greg Eisenberg. And then this little bit here

Eisenberg. And then this little bit here just tells it to actually spin it up like to publish it on the web uh in a preview mode so we can see what it's done. So I'm going to run that.

done. So I'm going to run that.

>> So So this is this is um Claude Code.

>> Yes. Yeah. Right now we're in Claude Code and this is just accessing it through the desktop app for Claude.

Um, so I'm just going to run that.

And then I'm also going to give the same prompt to codeex here. So this is the codeex app. And you can see same

codeex app. And you can see same concept. It says let's build. We can

concept. It says let's build. We can

choose a folder on our computer to work in like demo 2. And then we're going to give that a prompt as well. And we're

going to tell it to host it on a different one.

And then also in anti-gravity. So you

can see same concept. We're going in selecting a folder and then we will give it the prompt as well.

>> How should people think about security and these different products?

>> I like to think of security as in just like scoping what they have access to.

So, by default, anti-gravity cla code and codeex, they're very very secure because they're built by these massive companies that have a lot on the line um

to protect. And I just, you know, if you

to protect. And I just, you know, if you if you're building out these agents to manage different elements of your business, like the other week I built one um that does manages meta ads and

obviously that's quite a risky thing to give an agent control over managing ad budgets. So it just comes down to like

budgets. So it just comes down to like what you feel comfortable giving the agent and also you can control what privileges or you can control what um tool permissions it has access to so

that if it was compromised for whatever reason the worst case like isn't that bad and that means you know just giving it like readon access to certain important platforms and stuff like that.

Does that make sense?

>> Yeah totally. I mean comparing it to like open claw which is like >> way >> which I want to touch on at the end as well cuz that's same thing just another harness but it's just like the wild west.

>> Cool.

>> Uh and one thing like a nice little analogy to think about these harnesses is what we're going to learn today is we're going to learn to drive. So we're

going to learn about how to you know steer the car like how the pedals, the brakes work, the accelerator works, the handbrake. But then once you know how to

handbrake. But then once you know how to drive, you can kind of jump in any car, whether it's like a old Toyota, a Range Rover, and you inherently sort of know what to do. And that just comes down to understanding all these key concepts

that we're going to go through today.

And you can think of the agent harnesses like different cars. And some of them will have better features like seat warmers and cruise control, but it's all once you know how to drive, you can pretty much jump in any of them and use

them. So, we've just got our thing over

them. So, we've just got our thing over here building t building the website for Greg and it's going through this agent loop right now. So, you can see here it's actually decided that it's going to

launch um an agent to go and research Greg Eisenberg and I've connected it up to Plexity. So, it's now using Plexity

to Plexity. So, it's now using Plexity to research Greg. So, it's going through its first step of the loop. And I

imagine that uh Codeex has also done something similar here. You can see it's still working, but it's gone.

And um started to build this out through the loop. I think Claude code does the

the loop. I think Claude code does the best job of actually displaying that loop um and allowing you to see what it's thought about compared to anti-gravity and codeex, >> but it's all just going through the same

sort of loop process that I described earlier. And I think so when you when

earlier. And I think so when you when you say you hooked it up to Perplexity, didn't it's not like you asked it to hook it up, right? It just sort of did it.

>> Yeah. Because I've um I've given Claude code perplexity as a tool via MCP, >> which we're going to get into um very very shortly. All about MCPs, which is

very shortly. All about MCPs, which is just connecting tools up.

>> So we can see that in anti-gravity, it's gone. You can see this thinking

it's gone. You can see this thinking process. It's gone. Um,

process. It's gone. Um,

I'm now examining the current directory to figure out if there's an existing project or if I build one from scratch.

It's then going um I'm now going to start to build this thing and then it's built the the website and it's given us a little local host preview here. So,

it's created this nice little portfolio site for for you, Greg.

>> What's interesting is like it's super minimalist and I mean it it did its job, right? like it. This

is >> I would totally launch something like this.

>> It actually looks really nice. Did it

Did it scrape your email address?

Correct.

>> That's That's not my email address. And

I don't live in Canada anymore. So, but

yeah. So, there's a few copy things, but other than that, uh >> yeah, it's done a pretty good job.

>> It did.

>> And if we go So, that was anti-gravity.

Um, if we go into Codeex as well, you can see here it's finished doing its website, which is somewhat similar.

>> I think I prefer Geminis's.

>> Yeah, I agree.

>> And if we check out Claude as well. Um,

it's still going. You can see this loop, right? It's gone. Okay, first off, who

right? It's gone. Okay, first off, who is Greg Eisenberg? It's gone and researched Greg, then fed it back into that observe step, and it's gone, all right, what next? Now I need to create

the HTML file. So it's written the the code and then now it's gone. Okay, so he wanted it spun up on this local server.

So now I'm going to spin it up on the server. And then the last iteration of

server. And then the last iteration of the loop is to check that it's actually done and can conclude the task is complete. It's opening it up and

complete. It's opening it up and screenshotting the website and then reviewing the screenshots to check that the website is complete. And you can see here it's done another pretty good job.

This one's very similar to the Gemini one. Hey,

one. Hey, >> that's true.

Um, but yeah, that's just like demoing how that loop is actually working in real time.

>> Yeah. I mean, what comes to mind just by watching this is like how many people on the planet would benefit from a very clean website and like >> Yeah.

>> How how do you set up these agents so that like, you know, maybe it's like a cold email loop, right? Like you're

sending cold emails. Hey, I built you this website so and so business. Do you

want it? It's going to cost $250.

>> Yeah. Yeah, that's actually a great idea. Um, pre-making websites for

idea. Um, pre-making websites for companies and it's like an offtheshelf thing. It's like, "Hey, I made you this

thing. It's like, "Hey, I made you this website. If you want it, like if you

website. If you want it, like if you want to own it, it's $250. You can just do a mass cold email thing."

Uh, cool. So, I think that's like pretty much illustrated that agent loop example. So, I'm just going to go um

example. So, I'm just going to go um back to our trusty board over here. But

you can understand that it's just like all of these apps are just different flavors of the same thing. And then what we're going to be working up to today is my workspace looks something like this

is I have, you know, a big like a a folder for each company or or client that I'm working in. And then I'll have folders underneath with all my heads of departments.

And then um within those heads of departments, I'll have skills and MCPs, which we'll get into, and context. And

then I've got like an overarching one at the top to just to sort of manage them all. But we're going to be focusing

all. But we're going to be focusing today on building out this executive assistant to take care of just your manual day-to-day tasks and free up at least one to two hours extra per day.

Um, cool. So to build this out like we did uh with our demos, it's running off your local files. So we're going to create a folder here called executive

assistant.

And also through building out this assistant, it's going to allow us to clearly explain each of the concepts um of building an agent in real time. And

the way I like to think about building agents is on boarding them like a real employee. So, if you took on a real

employee. So, if you took on a real executive assistant, you couldn't expect just for them to come into the office and you to give them a task without explaining your business first, your

clients, what you do, the tools, um because they just would not be a very good executive assistant. So, that's the first step that we need to go through uh when we're building out this agent. So,

uh I'm actually going to work uh in co-work at the beginning. So

co-work is just another agent harness to do pretty much the same thing as all the others just that loop connecting in your tools and the context. So you can see here um this was my little uh previous session where I was building some

diagrams but we can go um and you can follow along include code or codeex or anti-gravity or whatever Asian harness that you want to work in. But I just think that co-work has really nice

simple UI for people to just understand really well what's actually going on.

So, we're going to open up this executive assistant folder.

And you can see here that if we ask it, write me a cold email >> and send that off.

>> So, pe So, people are going to ask how how did you transcribe? You did like a voice to text.

>> Yeah. So, that is um I use one called Monologue, but there's a lot out there on the market. Whisper Flow is another popular one and it just allows you to hold a little button on your computer

and just yap away and it will just transcribe it neatly into text >> and I find >> it looks good. Monologue looks really good.

>> Yeah, I think it's built by the team at every every >> um it's a cool product but uh so what it's asking so it's it's straight away we've it's got no context here. So,

it's working out of uh that folder here on our computer, but there's nothing in the folder, >> and it has no memory of our previous sessions. Um, and it's asking like what

sessions. Um, and it's asking like what like what do you even sell? Um, and then we got to kind of give it like who do you target? What tone do you want? This

you target? What tone do you want? This

is all things that our executive assistant should know. Um, so I'm just going to stop the response there. And

one thing that's really important to know which might be a bit of a shock moving from chat to agents is that these agents memory work a little bit different. So if you're used to using

different. So if you're used to using chat models like chat gbt and claude if you open up a fresh session in the one of these chats you don't give it any context. You don't upload any files and

context. You don't upload any files and you just say who am I and what do I do?

It's going to know a scary amount about you. And that's because with these chat

you. And that's because with these chat models they have memory built in automatically. So every time you sort of

automatically. So every time you sort of say things that are important, the chat model saves it to its memory in the cloud that you can't see and you can't control. And with agents, you have to

control. And with agents, you have to set up memory and control exactly what you give it. And I think that's actually a benefit, not a limitation. Because

what happens is if you're using chat GBT and it's got the auto memory, you're having conversations about three different companies, maybe you're asking for relationship advice, and then all of

a sudden, um, when you ask it to write a landing page copy, it's pulling in context from all these other places that you don't really want in there. So, with

these agents, um, you need to actually set up that context and memory. So, as

you can see, when we asked it to write a cold email, it just had no idea about anything. So, we need to give it a

anything. So, we need to give it a context file. And the way you do this,

context file. And the way you do this, right, so you can see this example here.

It doesn't know anything about us. And

that's because we haven't populated what's called an agents.md file. And an

agents.mmd file is just like a system prompt. Just like if you've created any

prompt. Just like if you've created any custom GPTs before, you have that field for custom instructions or in the project like I just showed before, you've got that field for custom instructions and it just gives it this

context that's kind of always there, always on. And you put in there things

always on. And you put in there things like its role, context about you, um your preferences for working. And then

what happens is every new session before it answers your your query or task it loads in all this context to its brain as part of that observe step in the

loop. So I have pre-prepared

loop. So I have pre-prepared pardon me.

So I've pre-prepared a uh agents.mmd file here. So if we drag this in over here, this uh when you're working within claude code, it's called

a claude.mmd. When you're working within

a claude.mmd. When you're working within Gemini, it's called a gemini.md. But

when you're working within codeex or openclaw, it's an agents.mmd. But it's

all the same concept. So we can drag this into our folder here. And if we open up this file for a little preview,

we can see here I've got in here um all about me, what my business does, my working preferences, like the tools that I use and what for like notion project management,

>> Stripe. Um

>> Stripe. Um we've got, you know, all the information, my item customized loaded with context here. And I pre-prepared this, but and if you want to make one of

those, you can just use called chat or co-work. and you can ask it to help you

co-work. and you can ask it to help you build out this uh agents.mmd file and to just ask you interview style questions to extract all the context from you and

then build the file. So if I jump back in now, if I go to a new task, same folder, and we say

write me a cold email, it's going to have all that context.

>> Yeah, that's what we hope.

>> That's what we hope. Here

we go. And it knows, you can see this files over here. It knows automatically to load in this file if you title it correctly.

>> Yeah, it's basically just like a a reminder file.

>> Yeah, pretty much. It's just like loading it in so it has all this set context before you even start working.

And one of the other big shifts to make which comes with moving from chat to agents is prompt engineering used to be the big thing. It was like, "Here's the ultimate prompt for going viral on

social media or use this prompt for this." And now it's all about context

this." And now it's all about context engineering. It's about how well can you

engineering. It's about how well can you load up your agent with all the information about your business so that your prompts can be stupidly simple like write me a cold email and you're still

going to get an amazing result. Um, you

can see already here it's already asking like um is it a brand or sponsor, potential partner or consulting client.

So, it's already got that context. um

book a call.

Um you know, it's it's it's loaded in everything that we've given it from that agents.mmd file.

agents.mmd file.

And then now we've got a a pretty decent cold email there ready to go.

So that's basically agents.mmd files for you. And you want to create one of those

you. And you want to create one of those to onboard your agent with all the context it needs. And if you have lots of context, without getting into too many advanced concepts here, sometimes

what I will do is I will create like a um a folder called context.

Load that in. And in here, it's got different files about me, brand, voice, idle, customer profile, etc., etc. And then in order to keep this smaller, I

will then just say in this cloud. MD

file um before answering any questions or before doing any tasks read my context folder to understand about myself and my business because by default if you just have this context

file in here but no claw.md it won't load all that into the session by default but if you tell it in this file that it always loads in to then check this file you can start to like string

all your context together and a lot of people have done that with obsidian so they'll have like in their claw MMD file, they'll tell it to go check their Obsidian vault for their second brain to go and find context.

>> Mhm.

>> So that is agents.mmd files explained.

So that's how you actually when you're onboarding your agent like our executive assistant, you can train it up on who you are and your business. And then as you can see here, you know, I've got folders for all these different roles in

my business. And in the head of

my business. And in the head of marketing, that claude.md file would look somewhat similar, but in the top it would say instead like you are my head of marketing. you speak like this. These

of marketing. you speak like this. These

are your tasks. These are your roles.

And then the second thing here is about memory and the self-improving loop. So,

we've solved the problem now. Um, try

not to get too dizzy with me switching tabs, but we've solved the problem of our executive assistant not knowing anything about us or our business. But

now we have a new problem, which is it doesn't really remember the intricate details or your preferences across sessions unless you're manually going and updating that claude.md file. So you

can see here if we go um my favorite color is lavender.

It'll probably say something like got it noted.

>> Yeah, that makes sense, right? Cuz it's

and it's it's adding where where's that adding it?

>> Well, it's not adding it. That's the

thing. So we can tell it my favorite color is lavender and it's gone. The

users just shared that's that thinking step. It's like the users just shared

step. It's like the users just shared >> this like no nothing needed. Good to

know. I'll keep that in mind.

>> But then if we go into a new session, same folder, and we go, "What is my fave color?"

fave color?" Mind my spelling.

It's going to say, "I have no idea what your favorite color is." Even though we just told it. And that um is an issue, you know, because if you're working, you know, uh you've got like a head of sales

or something and it keeps it signs off your emails wrong and you tell it, you correct it and you say, "Never sign off emails with cheers. Say warm regards."

And it will go, "Okay, got it. Noted."

But then the next day you start working and it does the same thing again. It's

like my agent's broken. But really, it's not. It's running off those context

not. It's running off those context files in the back. And unless you are manually updating it, it won't know to save that preference. So what I like to

do is I like to add in something like this to my agents.mnd

file. So this is just a little simple thing. You can pause the video and copy

thing. You can pause the video and copy it, but I like to I'm just going to remove that context file for now. That was just to illustrate that example of adding more. But we're just working with this

more. But we're just working with this one file for now. So, I'm just going to open this up so I can edit it. And I

will quite often add something on the bottom like that little snippet. And

this basically just says, actually, you know what? I might just add it at the

know what? I might just add it at the top just so it's there top of mind for my agent because I think this is really important.

So, you can see I've just added this in.

It just says read all files in context.

readmemory.mmd. This is what you've learned over time. And then when I correct you or you learn something new, update the relevant section in memory.mmd. And it's just got a couple

memory.mmd. And it's just got a couple little things here. And it just says keep memory.md current. When something

keep memory.md current. When something

changes, update it in place and replace outdated info. So we can do command S to

outdated info. So we can do command S to save that.

And then I'm going to add another file here. We can actually just duplicate

here. We can actually just duplicate this.

And this one I'm going to call memory.m

MD.

And then we can open up this one. And

I'm just going to remove all this context here.

Um, except I'm just going to keep those sections.

So memory MD is basically I mean it just it's what it sounds like, right? It's

basically like you want to, you know, if the goal is to build, you know, AI employees that do things for us, they're going to need to need to remember our preferences, right? A good

employee remembers preferences.

>> Exactly.

>> And learns over time and not that compounds. So memory.md

compounds. So memory.md

is just a place that you can just make sure that uh over time it you know your whatever you're using co-work or whatever it ends up it ends up getting compounded getting smarter. So

ultimately >> you might be trying things like co-work and and you're you're not getting good results and a big part of that is you don't have uh clone MD and memory MD sort of

>> Yeah.

>> set up. Exactly. Exactly. And now the thing is some of these agent harnesses have started to add in this memory system that we're doing manually telling it to update. Some of them have have got

that built in automatically like open claw and I believe like manis and some of the others have that built in automatically but it's still important to understand because it's just doing the same thing under the hood except

they've just set this up for you. Um, so

we've got this here now. We got our memory. MD, our claw.md. And then now if

memory. MD, our claw.md. And then now if we go back into co-work. If we do a new session in that

co-work. If we do a new session in that same folder and we say my favorite color

is lavender.

Better remember.

>> For the sake of the demo, I hope that it that it does what it's told. it it's

going to remember it.

>> You can see here, >> perfect. It's gone. Good. I'll remember

>> perfect. It's gone. Good. I'll remember

that. Let me save it to memory. And now

you've got this big memory file that builds up over time. And whether, for example, this is your executive assistant. So it might be saving

assistant. So it might be saving preferences like how to sign off emails or don't connect with clients on Slack.

I always want to keep client coms on email. But if you're building out like a

email. But if you're building out like a head of marketing, it might be preferences about how you like your ads structured in Facebook manager. If

you're building if you've got a folder where you're working on a website or an app, it might be things like don't use dark mode and then it will update. So

it'll never use dark mode again. And

they just compound over time. So as you start to build up these rules, >> the amount of errors go down and and this just compounds and compounds over

weeks and months. Remy, have you seen some of these memory MD files get so big that at a certain point it's just ineffective?

>> Great question. I personally haven't had that happen to me yet. I haven't hit that threshold.

>> Um, but a best practice for those claw.md files is to keep it around like

claw.md files is to keep it around like no more than 200 lines. And yeah, I could imagine if you started to build this up over years and years, you'd eventually hit a point where all the the

rules are stepping on each other's toes.

Um, and you know, you could probably go through and do a bit of like a manual clear, but I haven't hit that threshold yet.

>> Cool. So, people don't need to worry about cluttering their memory.

I wouldn't worry too much. I mean, if it's saving like this, like the silliest little things like the tiniest corrections, you can maybe update that claw. MD to say only save like

claw. MD to say only save like substantial corrections, you know, and then you can have a bit more control about what it's saving.

>> So, that's probably Yeah. Yeah. What I

would do there. But once you've set this up, now when you say something like quit writing so formally, it's going to do the task then update it agents.mmd or in

this case claw.md to keep tone casual, never formal. And then now in any new

never formal. And then now in any new sessions, it's going to keep that preference over time, which is pretty cool. So now we've got our executive

cool. So now we've got our executive assistant set up with uh memory and we've given given him his role. We now

need to connect our tools because by default, most of these agent harnesses, they just um have web search baked in.

But if you want to actually start linking it up to your tools like Gmail, Calendar, and everything else, which is where the real productivity gains are made, you need to do so uh via what's

called MCP.

And I actually got Greg, I got this MCP explanation from when you had on um is it Ross Mike?

>> Ross Mike. Yeah.

>> Yeah. So this he did a great explanation and it just dropped into my head really nicely and it's basically that before MCPs your agent or your LLM in order to speak

to tools it had to kind of learn their language cuz Claude speaks English, notion speaks Spanish, Gmail French, your browser speaks Japanese and Slack speaks Chinese and it was capable of

connecting to those tools but it like required these extensive custom developments that took a long time. But

then uh Anthropic actually created MCP.

Is that right?

>> Yep, that's right. Yeah.

>> Yeah.

>> Anthropic built MCP to basically sit as this translator in between your tools so that Claude can still just speak English and your tools can just speak their

languages and this MCP speaks every language and then just translates your calls from your uh agent to the tool and then from the tool back to your agent.

So just set a really easy standardized way to connect tools up and that's what we're going to be using to connect all of the tools to our executive assistant.

So if we go back into co-work here, you can see that Claude make it really really easy to connect up your tools.

You can just go to connectors, browse connectors, and they've got like hundreds of all the like biggest apps that you probably use and you can just, you know, add them, sign in. Pretty

self-explanatory.

But I believe codeex would be the exact same. You know, you can go um skills or

same. You know, you can go um skills or um if we go settings, they probably have like a and then like Manis is the same.

For example, if you go into Manis, we can see uh we can go and connect our tools. Very very similar process. And

tools. Very very similar process. And

then same with perplexity computer. You

know, you got your connectors and you can connect all your tools in here. It's

just all using that um model context protocol MCP.

So, I've already before the episode gone and connected all of the tools that I use most like Gmail, Google Calendar, Granola, Notion. They're all set up

Granola, Notion. They're all set up already as MCPs. And what I'm going to do now is I'm actually going to um open up this executive assistant folder in Claude Code to sort of demonstrate how

these harnesses are all the same and they work off your local files. And the

real future proof AI stack is just having those markdown files on your computer. And the reason why I like to

computer. And the reason why I like to work in markdown files is because it's just the easiest sort of format um for your LLM, for your agent to actually digest and understand compared to if you

were to give it your files as like a docs or a PDF file. So I like to use claude code within Visual Studio Code.

Um so you can see here I'm just going to it looks very similar to anti-gravity.

I'm just going to open up our executive assistant folder here. And the way that I see the future of this all going, Greg, is I think that everyone's going to have their what I call an AIOS, like

an operating system. And this will just compound over time, like you saw with adding the rules and getting less errors, but with adding your tools and then skills, which we'll get into, which is basically just training AI on your

processes. And I think that everyone's

processes. And I think that everyone's going to have like an AI operating system they work in. And everyone will just have personal agents and agents to manage each department of their company.

And people won't actually use these apps anymore. Like I've connected up Gmail,

anymore. Like I've connected up Gmail, uh, Google Drive, Calendar, Granola for my meeting notes, Stripe for payments, notion for project management, and I don't even enter these tools anymore. I

just sit in Claude Code as one central place. And an example here is I sent

place. And an example here is I sent myself before the episode, I sent myself an email from a fake prospect and I also entered in Granola a fake meeting with

this prospect. So now I can say things

this prospect. So now I can say things like um summarize my inbox from today.

>> Um you know someone might ask like well how important is that really? You know

like is that such a high value task? Like are

what are high value tasks that you're actually getting done here?

So I'm >> or or maybe you get a lot of emails you know >> well if you know emails is a big thing if you do get a lot of emails but just

having like all those tools connected in one place and not having to switch and and copy paste context. So you'll see an example here, right? So we've got summarize my inbox from today, which is like one of the most basic agent tasks

ever. But we can see this is one I sent

ever. But we can see this is one I sent earlier. We've got this one email here

earlier. We've got this one email here like our call today. Excited after your call wants next steps. So I might just say here, um,

okay, great. I review my meeting notes

okay, great. I review my meeting notes with Maltoshi from today and then draft up the email sending the proposal and creating the Stripe payment link and

then go into notion and set up the project. And where this starts to

project. And where this starts to compound even more is when you start to build out skills for each of your processes. Because every time I do a

processes. Because every time I do a process even like this manually prompting it um and I know I'm going to do it again at some point, I'll then just turn that into a skill. Uh and then

you eventually end up if you automate like three to five tiny manual processes each week with skills, you eventually end up um automating like your entire life with these agents,

>> right? So it's it's not so much in like

>> right? So it's it's not so much in like summarize my my emails where it's super super valuable. It's like that's where

super valuable. It's like that's where the starting point is and then we want to like manipulate it and use it and go deeper and stuff like that. That's when

>> connecting the >> these tools really really are valuable.

>> And you can see here it's now connecting all my tools. So it's gone into Granola and found the the full meeting of what we went through today.

>> It's now going into Stripe to create the product link.

>> Um >> and then it's going into notion to set up the project and then it will it should create the draft ready for us to

go to send out. that this is really like a new way of working, right?

>> Yeah, it is. It is. It it it's so new and I and I even this task, it's really simple, right? Just sending an email

simple, right? Just sending an email based on a call with a proposal link and stuff. But like even if you can just do

stuff. But like even if you can just do something like seven times faster without having to go into all these tools, copy the meeting notes into the page to give it context on your meeting,

it really starts to compound. Then you

start to fit like a week in a day and then 7 weeks in a week. Um, and you stack that up over a year and you're going to be miles ahead of everyone else. Uh, and when we get into skills,

else. Uh, and when we get into skills, you're going to see how this continues to get even better. But, um, you can see here it's drafted the email. It's pulled

in all these insights from our call in Granola, which is like where I do my meeting notes. And then it's created the

meeting notes. And then it's created the Stripe payment link here, ready to go.

>> That's cool.

And then now I can just go um send this email and it will use my Gmail integration to to go and send it. Uh and

then >> that's really cool.

>> It is. Hey, I think this is the new way of working. And Cody Schneider, who you

of working. And Cody Schneider, who you had on the pod the other week, I saw a tweet from him and he said that in the future, everyone's going to have like an AI operating system like this and you're going to have like the 100x employee

because everyone will come into their role with a pre-existing AI operating system and then build out skills for all their manual processes similar to how I was describing and just keep building

skills each week for anything manual that comes up until eventually their entire life and work life is automated.

Great. As you can see here, it's now created the draft here in Gmail, ready for us to to go. And if we're happy with it in the platform, I could also just ask CL to send it there and then. Uh,

but then what like also gets really cool is I'm going to demonstrate now how I actually build out skills for these processes. So I know I've talked a lot

processes. So I know I've talked a lot about skills so far. I want to just give a little overview on what skills actually are.

>> Yeah.

>> So the easiest way to think about skills is SOPs for AI. So standing operated a uh standing operated oh my god standard operating procedures for AI. So it means

you never >> it means once you explain something once you never have to explain it ever again.

An example of this is without skills. If

you uh are creating a proposal for a client and you're sitting in in your claw chat or whatever agent harness you're using and you ask it to create this proposal you're probably going to go back and forth a bunch of times. uh

remove change the formatting here. Use

this color blue for this part. Um put

the price at the bottom instead of at the top. And eventually maybe after 15

the top. And eventually maybe after 15 minutes, half an hour, you land on a proposal that you're really happy with and you send it. And then next week you want another proposal written, but

unless you're going and finding the same session and working in that same session, it's going to completely forgotten all of these preferences. And

even if you have that memory system set up, these kind of things you don't really want clogging up your memory.

They're they're better off as skills, which is basically it packages up that process into a skill file. And in

thatskill file, it's basically just a a markdown file that explains the exact process that you went through. So you

could create a proposal skill and then every time you now you need a proposal written it just takes that skill knows exactly what to do and then you can have that proposal the same way every single

time.

>> So is a skill like a memory file? Like

what's the difference between essentially a memory MD and a skill? like is it just is this is a is a is it just like a memory MD file

for a particular job to be done?

>> That's pretty much like exactly it. And

all of these agent harnesses pretty much now have skills as a feature. So you can see here like if we go into um codeex

for example, they've got skills here. Um

same with Claude as well. And these when you're working with these agent harnesses that operate like mostly locally off your computer, um you can see here it actually operates out of

this hidden file called a claude folder skills. And these are all of the skills

skills. And these are all of the skills that I've created. There's tons. And if

we open one up, for example, um like this one here, let's find a good one.

For example, I've got this one here for writing viral hooks.

And in this skill we have a skill file which is basically like your memory.mmd

which explains the exact process for writing viral hooks.

And then it's also got packaged in here some references like hook formulas.

>> Okay. So wait so how did you create that skill?

>> Okay. So there's two ways that I find useful to create skills is one you can have an idea of a skill you want to create off the bat. So like viral hooks for example, I had this course on viral

hooks which I transcribed put it into Claude and Claude has this by default.

It has a skill creator skill added into it. Same with all of the major agent

it. Same with all of the major agent harnesses. They'll have a skill creator

harnesses. They'll have a skill creator skill. So it's kind of like skill

skill. So it's kind of like skill seption. You use the skill creator skill

seption. You use the skill creator skill and you say, "Hey, take this course on viral hooks and create a viral hook skill." And it can create it like that.

skill." And it can create it like that.

That's one way. Uh and then it will package it up nicely with that skill.md.

It'll do the whole thing for you.

>> Wait, the second >> you asked you asked it to take the course.

>> Yeah, I uploaded the course like the full transcript of the course >> literally >> and I said uh >> yeah based on this course on viral hooks

build me a viral hook skill >> and then I use that for my content team.

So just like we're building the executive assistant folder, I've got a folder called content team and I've got that >> that uses that skill for me. And the

second way to create skills is going through a process manually once with Claude and then if you know you're going to have to do it again like that proposal example, you can just say once

you've done the task, hey, create a skill for what we just did and it will package up that process you went through. And that's the second main way

through. And that's the second main way that you can create skills.

>> So in your viral hook example, if you go into that folder again, >> Yeah.

>> So you have a references folder.

>> Yeah. So that is probably like was that Yeah, let's open it up.

I'm just curious.

>> See here it's got a full thing about like >> So was this from the course is a bit screwed.

>> Yeah, this was basically from like a a course I put into it.

>> And did you ask it to create a reference folder? Like how should people think

folder? Like how should people think about >> No, it just did it. It just did it. So I

think what would be great is if we could actually um demonstrate building a skill live. Let's do it.

live. Let's do it.

>> Um, and so for example, like this process here, um, I might I could create a skill called like a daily brief skill, you know, that goes through and summarizes

like your calendar, your inbox, and your projects in notion and plans out your day for you in the morning. And then you can run that on a scheduled task because uh, a lot of these agent harnesses now

are starting to introduce schedule tasks. So you can just run it on 9:00

tasks. So you can just run it on 9:00 a.m. every morning. use my daily brief

a.m. every morning. use my daily brief skill to prepare me for my day. But I

think another cool one here just to show you an example, right, of how my new like how intricate I make these skills is let's just say for this fictional

meeting I had with this person. I might

say um can you draft up an email? I want to refer Moltoshi to my good friend Sebastian who has an AI automation

agency and can help them out better with their needs.

And then we can just go um Sebastian's email is and we can just say that right and then

now it's going to be able to take the notes from granola all the context and then draft an email connecting these two um a prospect with a friend and you know I have different

like referral things set up like that with people in marketing agencies and that's just like a little manual process there tiny maybe takes 15 minutes out of my But then I can just go,

I want you to use your skill creator skill and create a Sebastian refer skill so that whenever I ask you to refer someone to Sebastian,

you know exactly what to do and you know his email address and then that'll build out that tiny skill for us, tiny process, but it means I like I know in the future I'm going to

have to refer refer someone to Seb again. And even if this skill now saves

again. And even if this skill now saves me 15 minutes, another five or six times, >> they start to compound when you create skills for every single little process in your business.

>> Yeah. I guess it's like we should just be asking ourselves like you know in our day-to-day life like what are all the jobs to be done >> and what are all skills that we need

like what are the repet repetitive processes or SOPs as you >> talked about and then just setting up as many as possible right to make our lives easier.

>> Exactly. And just to give you a little demo here. So I sort of alluded to that

demo here. So I sort of alluded to that folder structure at the start of the video and this is it here. So I've got workspaces AI with Remy and for example

I can open up my content team and within this folder I this is just like an more elaborate version of our executive assistant but we've got our

claude.mmd in here which explains

claude.mmd in here which explains um you are like the main orchestrator.

you have these sub agents. It's just a more elaborate version of that claw.mmd.

But I've got a skill within this for like a meta ads analysis. So that was a process. For example, if you're a

process. For example, if you're a marketing agency owner, this is probably like the kind of stuff that you can get inspiration from. Um like ads analyzing,

inspiration from. Um like ads analyzing, you know, taking competitors ads libraries, breaking down all the creatives um and their landing pages.

So, I built out this ads analyst skill where I literally just do ads analyst and then I paste in like the ads library URL like that and I'll click run. And I

did an example yesterday with the Udy, which is a super large ecom brand, and it ran through and basically scraped all of it took screenshots of all the

landing pages. It went and scraped all

landing pages. It went and scraped all of the ads that they running, all like 220. It then did a full deep dive here

220. It then did a full deep dive here on all the ads, visual analysis, copy analysis, why did this work, what could be improved. It basically did a

be improved. It basically did a breakdown of all the landing pages with screenshots.

>> Mhm.

>> And it did a master report here about everything that's going on. So did just did a full breakdown. And that was like a manual process that I would have gone through when I used to run like my marketing agency. And that probably

marketing agency. And that probably would have taken me like three or four hours. And then I went through to build

hours. And then I went through to build out this skill. I went through the process once with Claude. Like I started a fresh session and I was like, "All right, go to this ads library URL,

scrape this, do this, do this, do this for like 2 hours." And then after I'd done the entire process, I just said, "Use your skill creator skill to make a

skill for ads analyzing and package up the entire process we just went through as a skill." And then now whenever I want to do that process again, I can just invoke the skill and and it knows what to do

>> which is pretty cool.

>> Crazy. Crazy.

>> Absolutely crazy. So So now the the refer Sebastian skill is live and now whenever I want to refer someone to Sebastian again, I can just say um yeah, refer to Sebastian and it will just

start to use it will use that skill.

That's example there tiniest process but you build like those up for all these little tasks you do dayto-day. And then

it just compounds and compounds and compounds. And I can already think of an

compounds. And I can already think of an idea here where you could then you can chain skills together. So you could example have like a a meeting prep skill

that prepares you for a meeting by researching the guest um and compiling some talking points. You might have like a podcast research skill, for example, Greg, for a guest that's coming on. And

you might also create a morning brief skill. And in the morning brief skill,

skill. And in the morning brief skill, you can say if there's any meetings uh coming up or podcasts in my day, use the podcast research skill

um to research the the the guest and you can like chain them together uh and build like some really really cool workflows.

>> Yeah. And you can have it so it sends you an email, right?

>> Yeah. Exactly. And then now these harnesses are starting to get more and more autonomous. like they're starting

more autonomous. like they're starting to add like you know in the car they're starting to add like cruise control and stuff like now within most of these harnesses you can schedule tasks

um like in co-work now and you can like for example this one here you can go new task and I could say like uh run my

morning briefing skill and then set that to go every every morning at 9:00 a.m. And then now it's like an automated workflow that like you've just got running every morning now, which is pretty cool.

>> Yeah, I'm I'm doing this right now. Like

for example, I'm I'm buying a new car right now and it's like a particularly unique >> like color that I want and feature set

and there's just none none really available. So, you know, every 3 hours I

available. So, you know, every 3 hours I I I'm scraping all the different car marketplaces >> and and then I'm getting a notification

that, you know, when something comes up and it's it's crazy, right? Like it

saves me.

>> I'm one of those people that like >> if I didn't have this, I would be spending an hour of my day just like checking >> religiously every single, you know,

CarMax and cars.com and Autotrader and all these websites and refreshing like a insane person.

>> Um, so yeah, the schedule great example there, but you know this like this is a skill that would be relevant for my executive assistant. Same with that car

executive assistant. Same with that car one. That could be a good executive

one. That could be a good executive assistant skill. But then I've got those

assistant skill. But then I've got those more elaborate skills built out for like, you know, my um content team, that ads library scraping one. And then um I've got like, you know, re a research

weekly research skill for my newsletter team. And that runs on a schedule every

team. And that runs on a schedule every Thursday morning to go and scrape like I've built the skill out. So it goes and scrapes Twitter and Reddit to find what's new in AI. Um but yeah, skills

are so so powerful. um combine them with like your MCP so it can use your tools and then you can start to just train up your agent on all the processes in your

business. And I did a um a buildout on

business. And I did a um a buildout on OpenClaw for a agent to manage meta ads and it went pretty viral and the the way

I built this was with all these key concepts. So open claw functions the

concepts. So open claw functions the exact same way. So I pro I hope it hasn't timed out but I've remote accessed into my open core dashboard

here and you can see it's just operating off an agents.mmd file in the back end >> but instead of the claw folder it's in a open core folder and then it's got a couple of these other ones here it's got

a memory MD it's got some of these other ones that it's added on like a soul which tells it its personality and an identity which tells it who it is but

it's that same concept of markdown context file connecting your tools and then creating skills. So that uh meta ads manager one

skills. So that uh meta ads manager one that went pretty viral, I just planned it out with claude. I was like I want to have this open core manage my meta ads.

Help me write the agents.mmd file to tell it you are my meta ads media buyer.

You do these processes and then I created skills. So I created a ad

created skills. So I created a ad creative skill. Um, so it knew to go

creative skill. Um, so it knew to go look in the Dropbox folder and create creatives. I created a copywriting

creatives. I created a copywriting skill, so it knew how to write good copy for the business. Um, and I just built out there's probably maybe 15 different

skills, and then I would combine scheduled tasks, which is cron jobs with skills and the context files, and then just give it all the tools it needed.

Um, and just following the same process as we just went through to build the executive assistant, I had an open claw meta ads media buyer, which was sick.

>> I love it. And for the beginner, are you like, would you recommend people, you know, use openclaw or should they be using co-work or manis and some of the ones you showed?

>> So, great question. Uh, I would say that openclaw is probably like one of the hardest to learn and set up of these harnesses. I would say coowork is

harnesses. I would say coowork is probably the easiest.

>> I think perplexity computer. You did a video on it. Um it's pretty simple too.

Same with man.

>> Um but I would definitely learn um and get comfortable using like claw code or um one of these other ones before I started to play around with open claw.

And I would also uh have all the processes built out in claude code first. So, for example, that executive

first. So, for example, that executive assistant over the next uh two weeks, I might build out a bunch of skills like the Sebastian refer skill, um like a

daily brief, meeting prep, etc., etc. And then once I'm happy with how it's all functioning in claw code, then I could look to migrate that into open claw where it has that more autonomous

nature to it. So, that's kind of how I think about using open core and those other harnesses.

>> Yeah. Cool. All right. Anything else you wanted to um cover.

>> I mean, like really there's no right or wrong way to run these. Like that was the executive assistant. I've got ones built out for all the other departments in my business and then other businesses I work on, I have the same. Um and you

can just kind of build out that structure with what works for you. Um

you've got like one other thing to mention is global versus project level, which I'll just go over super quick. So

like those skills for example um you can add them at a global level which means they apply to every single project you work in whether it's the executive assistant your head of marketing and

some skills you want globally because you might use them in every chat um like a um truncate skill that I created which

just makes whenever I want to make something shorter it makes it shorter without compressing the sentences but just removing sentences that don't to be there and that's something I want in every session. So I've got that in

every session. So I've got that in global but you can have project level skills like that um Sebastian refer skill I would not want that with my marketing head of marketing cuz it's

just like clogs up the context and you don't need it there. Um, so I would have that as a project level for example. And

you can have um global skills versus project skills, global uh claw.mmd

versus project claw.mmd. And same with MCPs. You can have global MCPS and

MCPs. You can have global MCPS and project MCPS. That's probably the other

project MCPS. That's probably the other concept um to go over. But look, other than that, that's pretty much the entire agents crash course. So, it's just that loop running in the back end to complete

your task and connecting in your tools, your context, and the LLM all in one place. Uh, and I would just say to to

place. Uh, and I would just say to to work out what roles you want to start to build out an agent for, go into claw or your favorite chat model and get it to help you build out those uh context

files through an interview style process. Just say, "Ask me questions to

process. Just say, "Ask me questions to build this out." I would connect all the tools that you need and then start building out the skills through daily use and then pretty soon you can have

like pretty powerful AI agents built for every single um aspect and department of your business.

>> Remy, thank you so much. I'll include

links uh in the show notes in the description where you can go follow him, get to know him a little bit better, and uh I appreciate you coming on and dropping some sauce. Thank you, man.

>> Thank you so much for having me on, Greg. It's fin.

Greg. It's fin.