Why does accuracy optimization fail?

The most common reason accuracy optimization fails: Treating accuracy optimization as perfectionism. Perfectionism is refusing to act until conditions are flawless. Accuracy optimization is improving the hit rate of actions you are already taking. The perfectionist never ships. The accuracy optimizer ships, measures the error rate, and adjusts. If your 'accuracy improvements' consistently prevent you from acting at all, you have crossed from optimization into avoidance — and the fix is to lower your accuracy threshold, not raise it.

The fix: Pick one agent (habit, routine, decision rule) you run at least three times per week. For the next five instances, score each outcome on a simple 1-5 accuracy scale: Did it produce the result it was supposed to? Not 'did it feel good' or 'did it happen fast' — did it hit the target? Calculate your accuracy rate. If you score below 4.0 on average, identify the single most common error type. Design one specific check — a pause, a verification step, a reference to criteria — that would catch that error before the agent completes its action. Install the check. Run five more instances and rescore.

The underlying principle is straightforward: An agent that acts fast but wrong is worse than one that acts slowly but right.