What should govern a self-improving AI-agent loop?

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self-improving agent-loops governance ai-safety production-systems alignment

Summary

The author discusses the need for a fourth governance loop in self-improving AI agent systems to prevent objective drift, proposing periodic human review, withheld benchmarks, and rotating evaluators as practical controls.

I run production systems with three loops: a runtime loop that does the work, a reviewer that proposes improvements, and a persistent layer that carries accepted changes forward. Together they create variation, selection, persistence and iteration. The uncomfortable bit is that all three ask how to improve. None asks whether an improvement should survive when it lifts the score but shifts the real objective. I have started thinking of that missing governance layer as a fourth loop. The practical controls I keep returning to are periodic human review of apparently-good cases, a held-out benchmark the system cannot optimise against, and rotating evaluators so one model family is not always judging itself. I have not solved this cleanly; I wrote the essay to make the gap explicit. How are people running agent systems deciding when a measured improvement is actually drift? Full essay and sources in the first comment.
Original Article

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