Making my Own Agent, Part 2: The Agentic Loop
Prelude In the previous post I talked about the basics of an agent and why I decided to create my own. While I encourage you to go back to read the previous post, here's a refresher: an agent allows an LLM to interact with the outside world. I'm doing this for two main reasons, the first being that there is no great open source AI troubleshooting agent today and the second being that I find it helpful for my day-to-day activities. I talked a little about the technical parts in the earlier post but it was all generic and pertained to all agents, in this post I am going to talk about how my AI agent works, from how it receives a signal to how it investigates to how it escalates or presents its RCA. Chain of Thought Traditionally, LLMs worked that you asked a question and it answered immediately by just spitting out an answer. Then in January of 2022 a paper came out detailing chain of thought (CoT), where you prompt the LLM to reason...