How AI Agents Handle Errors and Exceptions

Your agent will fail. The only question is whether it fails safely — or fails silently, then builds a whole chain of wrong actions on top of the first bad step. A chatbot that hallucinates gives you one bad paragraph. An agent that acts — calling tools, writing records, sending messages — can turn one error into a cascade. That difference is why error handling is not plumbing. It is the thing that makes an agent trustworthy enough to run in production at all.

Not every error deserves the same response

The guide's central rule, borrowed from LangChain's fault-tolerance thinking, is blunt: stop treating all failures alike. Transient failures — timeouts, rate limits, brief provider outages — get a retry. Errors the model can fix get fed back to the model with the problem named. Errors that need a human pause the agent. Confuse those buckets and you get the two classic disasters: retrying something that will never succeed, or escalating something a backoff would have fixed in seconds.

Detection comes before recovery — and most agents skip it

An agent cannot recover from a failure it never notices, and the quiet failures are the dangerous ones. Production systems, as Splunk's incident guidance notes, rarely crash cleanly; they degrade, producing fluent, confident responses that ignore safety instructions or hallucinate policy. That is why the guide insists on three detection layers: structured tool results that say success or failure in machine-readable form (not a useless "something went wrong"), schema validation that catches malformed output before it becomes an action, and tracing that shows every step, tool call, and result. If you cannot see the steps, you cannot diagnose the failure — and a confident-sounding guess is not a diagnosis.

Retries without bounds are a failure mode of their own

Retries are for transient failures only, with exponential backoff and a hard cap — LangChain's retry middleware defaults to three total attempts. An unbounded retry burns tokens, stalls the user, and can repeat side effects: retry a timed-out payment blindly and the "recovery" becomes a double charge. The same discipline applies to model repair loops. Send the exact validation error back, cap the attempts, and when the cap is hit, treat it as a failure: fall back, return a clearly labelled partial result, or escalate. A loop that keeps producing slightly different wrong answers is not persistence. It is a runaway process in a helpful disguise.

The real test is knowing when to stop

Escalation is not a failure of automation; it is the boundary that makes automation trustworthy. Escalate on repeated failure, on high stakes (anything irreversible or outward-facing — money, messages, deletions, access changes), on ambiguity, and on safety-relevant uncertainty. And a real handoff is not "the payment step failed." It is what was tried, what failed, and what state was left behind — including whether the outcome was checked before any retry was considered. The practical rule is reversibility: cheap, reversible reads can run automatically; anything destructive, public, financial, or hard to undo pauses for a person approving the exact action, not a vague summary of intent.

Design for degradation, not perfection

Individual tactics are not enough. Timeouts on every wait, circuit breakers that stop hammering a failing dependency, separate caps on tool calls, tokens, and spend — with an explicit stop-and-handoff path when a cap is hit, because a cap on its own escalates nothing. Least privilege, so a read-only tool cannot write. Idempotent tools, so a safe retry cannot double-apply. And fault-injection testing: a tool that always times out, a model that always returns malformed output, a provider that dies mid-run. An error-handling design never exercised under failure is a hypothesis, not a safeguard.

The checklist is short, and most teams fail at least one item: classify before you respond, detect with structure rather than hope, bound every loop, escalate with a complete handoff, and log enough to investigate. Done well, all of this is invisible — the user just sees an agent that sometimes pauses, sometimes asks, and sometimes returns a partial answer with its limits stated. What they do not see is the chain of silent failures that never happened.

Go deeper

Read the full guide, including the failure-type map and the complete error-handling checklist: How AI Agents Handle Errors and Exceptions

Originally published on Agentic Affairs (https://agentic-affairs.com)