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An AI system running autonomous agents encountered costly retry loops where repeated failures led to excessive token usage, API calls, and operator attention without progress. The solution was to implement strict runtime policies that limit retries, define success criteria, require verification, and emit detailed receipts of each run. This approach stops the agent early on repeated identical failures, preserving operator trust and reducing wasted resources. The system also plans to improve failure classification to choose better next steps instead of blind retries.