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 rule