How AI Agents Handle Errors and Exceptions

AI agents are prone to errors, but how they handle those failures determines their trustworthiness in production. The key distinction is between safe failures, such as a chatbot hallucinating, and silent failures that can lead to a cascade of wrong actions. Error handling is not just about error management, but about building trust in an agent.

The guide emphasizes treating different errors uniquely: retrying transient failures like timeouts or brief provider outages, feeding back errors the model can fix to itself, and pausing the agent for human intervention when necessary. Failure detection precedes recovery, and most agents fail to notice the quiet, dangerous silent failures that degrade production systems.

Three detection layers are critical: structured tool results that provide machine-readable success or failure indicators, schema validation to catch malformed outputs, and tracing to document every step, tool call, and result. Without visibility into these steps, diagnosing failures is impossible, and a confident-sounding guess is not a diagnosis.

Retries are restricted to transient failures only, with exponential backoff and a hard cap to avoid token burn, user stalls, and repeated side effects. Model repair loops also require careful management, with exact validation errors sent back, a cap on attempts, and a fallback or escalation when the cap is reached.

Escalation is a controlled boundary, activated on repeated failure, high-stakes irreversible actions, ambiguity, or safety-relevant uncertainty. It should be a complete handoff detailing what was tried, what failed, and the system state left behind. A cheap, reversible read can run automatically, while destructive actions require human approval.

Designing for degradation, rather than perfection, involves timeouts on every wait, circuit breakers to halt attempts at failing dependencies, and separate caps on tool calls, tokens, and spend. Idempotent tools ensure safe reattempting, and fault-injection testing validates the error-handling design under failure conditions. When executed correctly, error handling becomes invisible to users, who only notice the agent's ability to pause, ask, or return a partial answer with limitations clearly stated.