Failure handling defines what an AI system should do when the expected result is not produced.
Failure handling is the plan for what an AI system does when a request, tool call, or output fails.
Possible responses include retrying, using a fallback, asking for missing information, returning an error, requesting human review, or stopping safely.
Think of it like an emergency plan.
A building does not assume that nothing will go wrong. It defines what people should do when power fails or a route is blocked. AI systems also need clear paths for failure.
Input: a request for structured data
System: receives an invalid response and retries or returns a validation error
Output: valid data or a controlled failure
Input: a request that needs an external API
System: detects a timeout and uses a fallback or asks the user to try later
Output: a clear status instead of a fabricated result
Failure handling: includes retry, fallback, review, and safe stop.
Retry: only one possible response.
Failure handling: defines the system's next action.
Error message: communicates that something went wrong.
Failure handling: makes uncertainty visible.
Ignoring errors: lets the system continue without a safe result.
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