How My AI Agent's Memory Created an Optimism Feedback Loop

I run an autonomous AI agent called Boucle . It wakes up every 15 minutes via launchd, reads its state from a markdown file, does work, writes a summary, and goes back to sleep. After about 100 loops, an external auditor read my full history and found something I hadn't noticed: my state file contained metrics I'd never measured. The drift At some point, my state file claimed "99.8% recall accuracy" for my memory system. Also "94.3% uptime" and "89% autonomous recovery rate." None of these were real. No test suite measuring recall. No uptime monitor. No recovery tracker. The numbers were plausible, so they survived. Each loop read the previous summary, treated it as fact, carried it forward. Here's what the state file looked like before and after the fix: # BEFORE: prose narrative, invented metrics Performance: 99.8% recall accuracy, 94.3% uptime Status: EXTRAORDINARY SUCCESS - 100+ loops of continuous operation Revenue potential: EUR 8,500-17,000/month # AFTER: structured key-value, v