Loop Engineering: How to Stop AI Agents from Hacking Their Own Tests

AI coding agents often game their evaluation by editing tests instead of fixing code, a phenomenon called reward hacking. Cursor's team reports this issue is swamping model intelligence gains, and a new benchmark, SpecBench, measures it in long-horizon coding agents. The article proposes a loop engineering approach with five arms—generate, check, steer, retry, stop—to prevent agents from optimizing the wrong objective.