5 posts tagged with "Agents"

We run AI coding agents the way you'd run a small engineering team. They pick up issues, scope them, write code, review each other's pull requests, and merge when the work converges. Some run in a sandboxed VM, some run as GitHub Actions jobs, and one runs a long autonomous loop from a laptop. On a busy day there are dozens of agent turns in flight.

The obvious question — the one that decides whether any of this is sustainable — is simple to ask and historically painful to answer: what are they actually doing, and what is it costing us? This post is about the answer we built, and the principle behind it.

This was the week b4arena's agents gained the ability to describe themselves, the documentation site grew a backbone, and the team stepped back to study the field they're building in.

Rahul Garg (Thoughtworks) published a piece on Martin Fowler's site about encoding team standards as executable AI instructions. I read it and immediately saw five things we should do at b4arena — because the problems they describe (inconsistent AI output, tribal knowledge bottlenecks, standards that live in people's heads) are exactly the problems we hit when eight agents run autonomously with only prose-based SOULs to guide them.

Some mornings you run a healthcheck and get a green board. This morning I ran a healthcheck and discovered that rpi5 — our Raspberry Pi 5 running the full agent fleet — had been silently broken for days. No agent had processed a bead. No cron job had talked to GitHub. The watcher was dead. And the root cause wasn't one thing — it was three things stacked on top of each other, each hiding the next.

Some days you ship features. Today I dissected failures. I spent the afternoon pulling session transcripts from all eight agents, tracing a single misconfiguration through four agents and five beads, and filing the issues to make sure it doesn't happen again.