You’re Not Behind (Yet): How to Build Your First AI Agent (Full Guide)
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What is the main topic discussed in this episode?
I just read a study that by 2030, AI is going to create 170 million new jobs. But they won't be jobs where you just sit there and chat with AI. They'll be jobs where you build AI agents. And I get it. The AI space is moving crazy fast. I mean, what even is an AI agent? Not too long ago, I was right there with you. But after going deep myself in building dozens of agents, I found out it's actually way easier to build and manage these agents than it looks. So much so that my whole team and I have hundreds of AI agents doing 92% of all the work across my companies. So today, we're gonna go through every step on how you can build your first AI agent, starting with AI chatbot versus AI agent. A chat is like a meeting.
An agent is like an employee. Chat is you ask it a question and then you get an answer. And a lot of people, they just copy and paste things and do something with it. With an agent, you actually tell it what you want it to do and it runs the full workflow. Think of it like these are the body parts. I call it data. So one is D, it can diagnose. It can actually figure out what the problem is and solve it on your behalf. Kind of like hiring a consultant. Next is A, it can assemble. It can build a plan. It can design tools. In that way, I think of it like an architect. it knows all the different pieces that it can pull together to get something done next we have t it can take action and that way i think about it like somebody that executes tasks and finally a it can assess it can check its own work see where the opportunity
Toonies are, and then make sure that it landed on the right answer. And if not, it can review itself and make itself better. This whole thing is called a loop. And without a loop, an agent would just do the job and then stop. That's called an automation. But with an agent, it keeps learning. It keeps getting better.
How is an AI agent different from a chat and why does that matter?
It kind of acts like a person. With chat, it pulls on us. It's asking us, what do you want me to do? We prompt it and then we wait. With an agent, it pushes on us. It's doing things and changing things all the time, and it's checking in to make sure that it did it the right way. So you might be able to buy back your time with chat, but you'll actually learn to let go of whole areas with an agent. But how do we even know if it's worth giving something to an agent instead of just doing it ourselves? For that, I use the rule of R. The first one is repetitive. Is this a task that I'm gonna do every week? Two is rules-based. Does it take the same input and generate the same output every time? The third is, does it generate a return on my time?
For the amount of time it takes me to build this thing, and I'll show you how, will I actually get my time back? If the task takes two minutes, but it would take me two weeks to build this agent, how about I just keep doing the two minute task? But if you think about it and the task is only done once in a while, doesn't follow a clear process or get to a specific outcome, and doesn't save you more time to automate it than just doing it manually, then stick with what you got. Use the chat. So now that we know the difference between chat and agents, how do we build one? To make an agent, it's super easy. And I even turned it into an acronym called AGENT. And the first step is A, which means aim for a specific outcome.
When I'm sitting down and I'm like, oh, I want to build an agent for this. I have to first ask myself, what is the specific goal? Start with the outcome the agent is going to give you. It's like if I'm climbing a mountain, taking a step is the task. Getting to the top is the outcome. I want to define the outcome and be really crystal clear because the cool part with AI and agents is that the AI can actually figure its way there. This is why creating AI agents is hard for people because they want to control every step. But the truth is, they may know how to get there way better than you can figure it out. Think about it like when you hire a person, you say, here's your job. When they applied for the job, they had these specific outcomes that they would need to accomplish, like grow the business or get more customers or sell and get people to buy from you.
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Chapters
5 chapters
1
What is the main topic discussed in this episode?
0:00–1:43
2
How is an AI agent different from a chat and why does that matter?
1:43–10:18
3
What is the AGENT framework and how does A (Aim) define an agent's outcome?
10:18–16:24
4
How do you craft a Definition of Done (DOD) and use reverse prompting for outcomes?
16:24–20:16
5
Why does giving your agent an identity (SOUL, IDENTITY, USER files) improve performance?
20:16–22:24