August 24, 2026
Agent Retry Policies That Control Cost
How to design AI agent retry policies that separate transient failures from expensive loops and stop spending when progress stalls.
Quick answer
A good agent retry policy retries transient failures, changes strategy after repeated errors, caps premium-model retries, and writes a blocked state when the same condition repeats.
Retries are reliability until they are waste
One retry after a network error is sensible. Five retries after the same permission failure is just paid confusion. Agent systems need to tell those apart.
Classify the failure before retrying
Timeout, rate limit, validation error, missing credential, stale selector, and test failure should not share the same retry path. Some need backoff. Some need a different command. Some need to stop.
Fallback retries need their own cap
If a cheap model fails and a stronger model fixes the issue, great. If the stronger model hits the same blocker twice, stop. Premium retries can turn a small reliability issue into the whole bill.
How Clawback helps
Clawback makes retry spend visible by workflow and model, so teams can spot loops before they become normal operating cost.
FAQ
How many retries should an agent get?
Usually one or two for transient failures. Repeated identical blockers should stop quickly.
Should failed runs be silent?
No. They should write a terminal blocked state with the next action.
What is the biggest retry cost mistake?
Letting every retry reload full context and escalate models without proving new progress.
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