Comment by dabaja

Honestly, it's empirical. We started with what was easiest to measure: human correction rate. If I had to step in and fix something, that's a clear signal the agent took a bad path. Iterations and reverts turned out to be noisier -- sometimes high iteration count means the task was genuinely hard, not that the agent made a mistake. So we downweighted those. The meta-answer is: pick the metric that most directly captures "I wish the agent hadn't done that." For us that's human intervention. For a team with better test coverage, it might be test failures after commit. For infra work, maybe rollback frequency. There's no universal loss function — it depends on where your pain actually is. We just made it explicit and started logging it. The logging alone forced clarity.