I Let an LLM Rewrite Its Own Prompt. The Real Win Was the Gate That Rejected It.

Previously: 9 Bugs That All Looked Like a Working System · I Built an AI That Rewrites Its Own Prompts · The Edit That Fixed 4 Tasks and Broke 1

I built an agent that can rewrite its own system prompt. The impressive part was not the rewriting. It was the refusal.

That's the flashy part. It is also the wrong place to start.

The optimizer can be an LLM, a heuristic, or random guessing. The gate must be deterministic, verifiable, and conservative. If the gate is wrong, the system drifts. If the gate is right, the system is safe — even when the analyzer is proposing garbage.

After 4,150 LLM calls, 716,580 tokens, 4 domains, 4 models, 12/12 Docker tests, 489 passing tests, and 26 closed v0.2.0 issues, here's what actually worked — and the 2 releases it took to prove it.

I Let an LLM Rewrite Its Own Prompt. The Real Win Was the Gate That Rejected It.

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