AI agent failures: why dashboards are green but agents lie for days

AI agents fail differently from traditional services: they don't crash but slowly degrade, making standard uptime and error-rate metrics useless. Agents make non-deterministic decisions at each step, so failures occur in reasoning cycles rather than in final responses. To achieve observability, teams must trace the entire reasoning cycle with a unique trace ID, measure agent-level metrics like tool selection accuracy and goal completion rate, evaluate a sample of responses using an LLM-as-judge, version prompts in Git, and write telemetry to two destinations (e.g., cloud provider and specialized tools) to avoid vendor lock-in. These practices are essential even before specialized agent observability tools mature.