What are Agentic Loops?

**Greg Isenberg** (0:00)
Everyone is talking about Agenetic Loops, but the reality is most people don't know what it is or how to use them. In this ep, I brought on Professor Ras Mic to clearly explain what it is, is it hype, is it real, and how to use it. And if you stick around to the end of the episode, he shows me the most concrete use case of Agenetic Loops that you can use starting today. Enjoy the episode.
Ras Mic, welcome to The Pod. By the end of this episode, what are people gonna learn?

**Ras Mic** (0:41)
You're gonna understand what a loop is. You're gonna understand why people are fanny out about it, and you're gonna understand why it is a terrible mistake, and unless you have money to burn, that you are not to do it. I'm also going to play the other side, and I'm gonna show you a loop that I use, but the general consensus, I think, is wrong, and we're gonna talk about it.

**Greg Isenberg** (0:59)
Okay, so by the end of the episode, people are gonna understand what an agentic loop is, why the most well-known people in the AI industry are obsessed about it. You're gonna keep it real with what we need to know about it and what we can avoid, and you're gonna show a real use case, a real example of how to actually use an agentic loop.

**Ras Mic** (1:19)
Exactly, exactly.

**Greg Isenberg** (1:21)
All right, bro.

**Ras Mic** (1:22)
All right, let's get into it. So as always, a lot of people love the diagrams, so we're just gonna start with diagrams. I paid, like, I think, $3 to get these stick figures, but I hope people appreciate them.
This is me and you, right? This is your average Joshmo who does not work at Anthropic or API or OpenAI. And this is Boris and Peter and anyone else who has unlimited access to models. Now, the way me and you have been working, this is what is called a human in the loop, is you and I will prompt our computer, right? Let's say this is our computer. Better yet, I'll say this is our AI agent, right? Whether you're using Cursor, Cloud, Codex, doesn't matter. You are prompting it yourself, right? You're telling it, hey, build me this landing page.
You know, build this feature X, Y, and Z. You are communicating with an AI agent, a platform of your choice via a prompt. And then a result is generated, right? A result is generated. And usually, what you and I will do is we will view this result, we will test this result, and we will keep on iterating. This is the loop where it goes back to us, right? So let's say I'm working on an app, and this app is a to-do list app, Greg. The first thing I'll probably want to do is I want to build up the landing page because I want to get this out to the public. So maybe they can sign up and join the wait list. So I'll prompt, say, build me a landing page, and let's say I like the landing page. Next, I'll work on authentication, and then once I'm happy with authentication, then I'll work on the backend. So this is what we are used to, and this, to be sharp with it, is called human in the loop, meaning it is the agent that's building, but it is you that is directing, governing, and allowing things to happen. What everyone has been talking about, particularly Boris and Peter, they said they don't write prompts, they generate, they build loops. Essentially, what they're talking about is, they're building a system, and I'm just going to show you here where this is the AI agent and then this is the result.
But instead of a human being in the loop, the human is in the loop one time, meaning it fires off said loop. But then the rest of the time, it's the agent generating a result. That result is then fed back into the agent. The agent then looks at the result and continues to work. Now, this in theory sounds cool because what essentially we're saying is, hey, I'm just going to have some spec.md file or some prd.md or whatever.md file, and this is going to be like a to-do list, a task list, and this is going to give all of the information the AI agent needs to build this.
Now, this sounds cool, and this low key might be the future, but here is where it goes terribly wrong. First and foremost, I want to paint this analogy. Let's say...

**Greg Isenberg** (4:38)