How to Build AI Agents That Check Their Own Work | Jared Zoneraich

Let the model cook and don't try to push it in certain directions. My agent's running for 9 hours straight now. It's crazy. More Devons are being launched from other Devons or programmatically than by humans. We're not trying to replace coding. We're trying to build abundance of good software and really multiply an engineer. You want the

engineer to bring taste and bring this high-level decision-making, but agents should be doing everything else and the agent shouldn't be bottlenecked on the engineer. >> All right. Hey everyone. I'm really excited to host Jared, builder in residence at Cognition today. Jared has a lot of background of building AI agents. So, going to ask him about, you

know, how to build a good AI agent, have him demo Devon's latest capabilities, and also talk about, you know, the pretty comparative AI coding and knowledge workspace. Welcome, Jared. >> Thanks for having me, Peter. >> Why don't we start with some high-level questions like, how do you think someone should think about building an agent

today? And what what's the number one mistake that one companies make when doing this stuff? >> I think first maybe I'd want to break it down into like two different types of build what building an agent means. And primarily, I guess the first one is building a user-facing agent or something that your users are

interacting with, a chatbot or something like that. And the second type is probably where my mind goes of an agent that you use. So, I spoke to a lot of DevOps teams recently that are building internal agents for their team to work better, which is like there's similarities and differences. I think when we're talking about an external