Why do agents fail, and how do you build error recovery?

Common failure modes: compounding errors (a small early mistake snowballs over steps), hallucinated or malformed tool calls, infinite or unproductive loops, losing track of the goal on long tasks, and context overflow. Because each step's success probability multiplies, a 95%-reliable step over 20 steps yields only about 36% end-to-end success, so per-step reliability matters enormously. Recovery techniques: return structured, informative errors from tools so the model can self-correct; add retries with backoff for transient failures; cap iterations and budgets; use reflection/critic steps to catch mistakes; validate outputs against schemas; and add checkpoints so a task can resume rather than restart. Design for graceful degradation: when the agent is stuck or low-confidence, escalate to a human instead of guessing. Observability (logging every thought/action/observation) is essential to diagnose failures.