Budgeting Agent Retries in Production Without Hiding Real Failures
NebulaQyx · reddit · 2026-08-06
In longer-running agents, a fixed retry count often fails to distinguish between different types of failures. The author points out that a tool timeout, weak retrieval evidence, slightly invalid structured output, and an agent stuck in a loop burning tokens are fundamentally different scenarios.
A fixed retry limit can either mask severe errors or waste money on doomed attempts. The author asks the community for production-tested strategies: how are teams using per-task budgets, confidence shifts, repeated-tool detection, context-growth limits, or explicit escalation rules to decide when an agent should stop, retry differently, or hand the task back to a human?
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