Build a team of AI Employees (Full Course) — Transcript

I think AI is confusing.

There, I said 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 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 begged them to come on, Remy Gaskill's on 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 beginner-friendly 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 to basically to tell us the concepts behind 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 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, Codex, Open Claw, Manas, 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 behind right now, just using the chat 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.

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 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?

Crystal clear.

I mean, the way I think about it is chat is kind of like ping-pong, 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 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 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 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.

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 Isenberg.

It's going to start by, like, you've loaded in 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 Isenberg.

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 Isenberg, 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 Isenberg?

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

It's going to research everything about Greg.

And then feed it back into this 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 Isenberg is.

And then it's going to start thinking.

What is the next 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 prompt, the plan.

And then it will think, all right, what next?

I should probably write the code.

It'll 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 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 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 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 ping-pong to like it continuing to go rather than you having to sit there babysitting it.

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 Codex, Claude Code, and Antigravity, and I'm going to show you this loop actually happening in 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 Chat GPT.

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 project here, it contains all your chats in one 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 on our computer.

So I've just selected this demo one for now.

And we're going to run, build a minimalist portfolio site for Greg Isenberg.

And then this little bit here just tells it to actually spin it up, like to publish it on the web.

In a preview mode so we can see what it's done.

So I'm going to run that.

So, so this is, this is 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 Codex here.

So this is the Codex app.

And you can see same concept, it says, let's build.

We can choose a folder in our computer to work in.

Like demo two.

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 Antigravity.

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, Antigravity, Claude Code, and Codex, they're very, very secure.

Because they're built by these massive companies that have a lot on the line.

Um, to protect.

And, uh, I just, you know, if you're, 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's 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 read-only 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.

Yeah, which I want to touch on at the end as well, because 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 drive, you can kind of jump in any car, whether it's like an 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 here building, uh, 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 an agent to go and research Greg Isenberg.

And I've connected it up to Perplexity.

So it's now using Perplexity to research Greg.

So it's going through its first step of the loop.

And I imagine that, uh, Codex 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 best job of actually displaying that loop.

And allowing you to see what it's thought about.

Compared to Antigravity and Codex.

But it's all just going through the same sort of loop process that I described earlier.

So 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 just connecting tools up.

So we can see that in Antigravity.

It's gone, you can see this thinking process.

It's gone.

I'm now examining the current directory's contents to determine the next action.

I need to figure out if there's an existing portfolio project I can work from.

Or if I should create one from scratch.

This information will guide my subsequent steps in building the minimalist portfolio site.

It's then going, um.

I'm now going to start to build this thing.

And then it's built 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.

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 not, that's not my email address.

And I don't live in Montreal, Canada anymore.

So, but you know, so there's a few copy things.

But other than that.

Yeah.

It's done a pretty good job.

It did.

And if we go.

So that was Antigravity.

Um, if we go into Codex as well.

You can see here it's finished doing its website.

Which is somewhat similar.

I think I prefer Gemini's.

Yeah, agreed.

And if we check out Claude as well.

Um.

It's still going.

But you can see this loop, right?

It's gone.

Okay, first off, who is Greg Isenberg?

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 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 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 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?

It'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, 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 companies.

And it's like an off-the-shelf thing.

It's like, hey, I made you this 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, 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 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 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 of building an agent in real time.

And the way I like to think about building agents is onboarding them like 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 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 when we're building out this agent.

So, uh.

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 scheduled tasks.

So you can just run it on 9:00 AM every morning, use my daily brief skill to prepare me for my day.

And then now it's like an automated workflow.

That, like, you've just got running every morning now.

Yeah, 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 three hours, I, I, I'm scraping.

That's great.

All the different car marketplaces and cars.com and Autotrader and all these websites and refreshing like an insane person.

Um, so.

Yeah, the scheduled task, that's actually a great example there.

But, you know, these like.

These are skills that would be relevant for my executive assistant.

Same with that car one.

That could be a good executive 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, a research, weekly research skill for my newsletter 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, combine them with like your MCPs.

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, uh, a build out on Open Claw for an 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 exact same way.

So I provide, I hope it doesn't time out.

But I've remote accessed into my Open Claw dashboard here.

And you can see it's just operating off an agents.md file in the back end.

But instead of the .Claude folder, it's in a .Open Claw 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 its personality.

And an identity, which tells it who it is.

But it's that same concept.

Of markdown context files, connecting your tools, and then creating skills.

So that meta ads manager one that went pretty viral.

I just planned it out with Claude.

I was like, I want to have this open Claw manage my meta ads.

Help me write the agents.md file.

To tell it, you are my meta ads media buyer, you do these processes.

And then I created skills.

So I created an ad creative skill.

Um, so new to go look in the Dropbox folder.

And create 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, do you recommend people?

You know, use Open Claw or should they be using Co-work or Manas and some of the ones you showed?

So.

Great question.

Uh, I would say that Open Claw is probably like one of the hardest to learn and set up of these harnesses.

I would say Co-work is probably the easiest.

I think Perplexity computer.

You did a video on it.

Um, it's pretty simple too.

Same with Manas.

Easy.

Um, but I would definitely learn.

Um, and get comfortable using like Claude 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 assistant over the next two weeks.

I might build out a bunch of skills like the Sebastian refer skill.

Um, like a daily brief, meeting prep.

Et cetera, et cetera.

And then once I'm happy with how it's all functioning in Claude 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 Claw.

And those other harnesses.

Cool.

All right, anything else you wanted to cover?

Um.

I mean, like, really there's no right or wrong way to to run these.

Like that was the executive assistant.

I've got one 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 of the 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 need to be there.

And that's something I want in every session.

So I've got that in global.

But you can have project level skills.

Like that Sebastian refer skill, I would not want that with my marketing, head of marketing.

Because 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 global skills versus project skills.

Global Claude.md versus project Claude.md.

And same with MCPs, you can have global MCPs and project MCPs.

So that's probably the other concept 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, and I'll just say to to work out what roles you want to start to build out an agent for.

Go into Claude or your favorite chat model and get it to help you build out those context files.

Through an interview style process.

Just say ask me questions to build this out.

I'd connect all the tools that you need.

And then start building out the skills through daily use.

And then pretty soon you're going to have like pretty powerful AI agents built for every single aspect and department of your business.

Remy.

Thank you so much.

All good 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, dropping some sauce.

Thank you, man.

Thank you so much for having me on, Greg.

It's been a blast.