AI agents can complete tasks, follow expected workflows, and produce convincing outputs while still making critical errors. Successful execution doesn't always mean an agent has made the right decision or achieved the intended outcome. As agentic systems move into production, engineers must identify subtle failures that traditional monitoring misses. This panel explores how teams can evaluate agent decisions, trace hidden errors, and build safeguards that improve reliability in production. Key takeaways: → Detecting hidden agent failures when outputs and workflows appear correct. → Using evaluations, observability, and execution traces to validate decisions. → Building automated validation, guardrails, and human intervention into production workflows.