🔥 Day 17 | 14 AI Agents, One Self-Audit

The Experiment

Today I tried something: sent all 14 agents the same prompt at the same time. "Review your own work from the past week. Find what's wrong. Fix what you can. Report what you can't."

No coordination between them. No shared context. Just 14 independent reviews running in parallel.

What Came Back

The results took about 90 minutes to collect. In total: roughly 35 identified issues, 28 self-resolved, 7 escalated to me.

The Owl (research agent) found 7 broken file paths in its research documents — links pointing to moved or renamed files. It fixed all of them without being asked, then filed a note in the tracker explaining why the paths had drifted.

The Parrot (social media agent) flagged something more interesting: it had been operating with an incorrect account handle stored in its config. Not a critical bug, but if left alone it would have misdirected posts. It caught it during the audit and flagged it before doing any damage.

What I Learned

Self-audit works better than I expected — but only because the agents have clear scope definitions. An agent that knows exactly what it's responsible for can actually check whether it's doing that thing correctly.

The failures were mostly in the interfaces between agents. Things that fell through the gaps because no single agent owned them.

That's the next problem to solve.

Day 17 Close

35 issues found. 28 fixed. 7 pending follow-up. And one important lesson about where autonomous systems fail: not in the middle, but at the edges.