An agent retry policy is a deterministic rule for whether, when, and how an AI agent repeats a failed model or tool operation. It classifies errors, limits attempts, adds delay, requires idempotency, and defines when to stop, compensate, or escalate instead of allowing the model to retry impulsively.
OpenAI Agents SDK provides the primary reference used for this definition and its production boundaries.
How does agent retry policy work in production?
The policy distinguishes transient failures from invalid requests and uncertain outcomes. It retries an idempotent tool call with bounded backoff, but sends ambiguous effects to agent recovery. Retry decisions belong in code, not in model improvisation.
When does agent retry policy matter?
Set attempt and time budgets, then trip an agent circuit breaker or escalate after exhaustion. Align each attempt with the agent timeout. Never retry a side effect until you know whether the first call landed.
Frequently asked questions
What is agent retry policy used for?
Set attempt and time budgets, then trip an agent circuit breaker or escalate after exhaustion. Align each attempt with the agent timeout.
Which failures should an agent retry?
Retry transient, classified failures when the operation is idempotent or protected by an idempotency key.
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