Data From 100+ AI Agents Shows They Fail by Silent Drift, Not Errors

AI agents in production tend to fail through silent drift in quality, cost, and speed rather than through explicit errors. An analysis of over 100 agents found that quality and token spend alerts were early signals for 38% of agents, while errors were an early signal for only about 1%. These problems are often persistent, and token spikes are frequently accompanied by other issues like a drop in output quality. Consequently, effective monitoring should prioritize these leading indicators instead of relying solely on error rate tracking to catch degradation early.