I've been in DevOps for a while. I have been in people management positions for 15 years and did very little coding. I am getting back into coding with help of AI. Lately I've been trying to figure out where AI fits into that role — not just running someone else's tool, but actually building agents myself.
LangGraph clicked for me because it maps to stuff I already know: DAGs, conditional branches, parallel stages, approval gates. It's basically a CI pipeline where some nodes happen to call an LLM instead of a shell script.
I built two projects back-to-back with it. Different problems, same patterns. Here's what I learned.
Project 1: Release Narrator
Writing release notes sucks. You read 200 commits, figure out which 15 matter, group them, write something that doesn't sound like a robot, and decide if it's a major or minor version. It's mechanical work with a few judgment calls buried in it.
