The single-improvement rule: how to stop your AI agent from breaking itself every night

You built a self-improving AI agent. It reviews its own performance every night, identifies problems, and makes changes. Then one morning you wake up to cascading failures and you can't tell which "improvement" broke things. This happens to everyone. The fix is one rule. The problem: multi-fix regressions Here's what a bad nightly review looks like: NIGHTLY FIXES APPLIED (2026-03-06): 1. Reduced context window loading from 12 files to 8 files 2. Changed tool call retry logic from 3 attempts to 5 attempts 3. Updated MEMORY.md compression threshold from 800 to 1200 tokens 4. Switched model from claude-sonnet to claude-haiku for classification tasks 5. Added rate limit guard to Stripe webhook handler Five changes in one night. The next morning, your agent's classification accuracy dropped 23%. Which of the 5 changes caused it? You don't know. So now you're rolling back all 5 — including the 4 that were fine. This is cascading regression. It's not a hypothetical. It's what happens when you