From Learning Capture to Self-Evolving Rules: Adding Verification Sweeps to terraphim-agent

From Learning Capture to Self-Evolving Rules: Adding Verification Sweeps to terraphim-agent A self-evolving AI coding agent sounds like science fiction. It is not. It is a shell script, a markdown file with grep patterns, and a weekly review discipline. We have been running terraphim-agent in production for months. It captures every failed bash command from Claude Code and OpenCode, stores them in a persistent learning database, and lets agents query past mistakes before repeating them. The capture loop works. The query system works. The correction mechanism works. What was missing was verification . We could capture mistakes and add corrections, but we had no way to prove the corrections were being followed. No machine-checkable enforcement. No audit trail. No quantitative measure of whether the system was actually improving. Then Meta Alchemist published a viral guide on transforming Claude Code into a self-evolving system, and two ideas jumped out: verification patterns on every rul