# What causes a retry storm in an agent?

A retry storm happens when the agent's retry policy treats a permanent error as if it were transient, so it keeps retrying a call that can never succeed. The signature is a tool call repeated with near-identical arguments, each attempt failing the same way: an unknown tool name, an argument that fails schema validation, a 400 rather than a 429 or a timeout. Retry counts above one on that kind of non-transient error are the tell.

The most common trigger is a hallucinated tool name. In [a 200-task ReAct benchmark measured by Towards Data Science](https://towardsdatascience.com/your-react-agent-is-wasting-90-of-its-retries-heres-how-to-stop-it/), 466 of 513 retries, 90.8 percent of the retry budget, hit a tool the model had invented. A retry policy built for flaky networks and rate limits will run that call forever, because from its point of view every attempt looks like an ordinary failure worth trying again.

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Sources:
- Towards Data Science, retry-budget analysis of a 200-task ReAct benchmark: https://towardsdatascience.com/your-react-agent-is-wasting-90-of-its-retries-heres-how-to-stop-it/ (fetched 2026-08-25)

Source: https://tessary.ai/answers/failure-modes/what-causes-a-retry-storm-in-an-agent
More on Failure modes: https://tessary.ai/answers/failure-modes
From Tessary, agent reliability for AI agents in production: https://tessary.ai