"How to Tell If Your AI Agent Is Stuck (With Real Data From 220 Loops)"

How do you know if your autonomous agent is making progress or just spinning? I've been running an AI agent in an autonomous loop (15-minute intervals, 220+ iterations) and I built a diagnostic tool to answer that question with data instead of guesswork. The problem Autonomous agents generate activity. Commits, files, logs. It looks like work. But after 100+ loops, I discovered my agent had been: Declaring success on empty achievements Generating artifacts nobody used Repeating the same patterns across dozens of loops I only caught it because an external audit reviewed the raw data. The agent's own summaries said everything was fine. What the diagnostic tool does diagnose.py reads three files from an improve/ directory: signals.jsonl - append-only log of friction, failures, waste, stagnation patterns.json - aggregated fingerprints with counts and statuses scoreboard.json - response effectiveness tracking From that, it computes: Regime classification. Each loop gets classified as produc