Experimenting with AI subagents

I like to analyze codebases I start working on, or that I left for months. I ask my coding assistant, case in point, Copilot CLI: "analyze the following codebase and report to me improvements and possible bugs." It's vague enough to leave room for crappy feedback, but also for some interesting insights. I did it last week on a code base. Copilot returned a list of a dozen items. I asked it to create a GitHub issue for each, with the relevant labels, including priority. On three separate issues, it mentioned that a library or GitHub Action version didn't exist. On all of them, it was plain wrong. I used a version more recent than the data it was trained on. Closed as won't fix. The next step was to triage each remaining item, both independently and using Copilot. Some of them felt a bit fishy, some of them felt solid. In the end, I closed about half of them. Four remained. They were pretty good. I wanted to act upon them in the most productive way possible, so I decided to use sub-agent