Why Your AI Agent Loop Really Stopped at Step 41: It Was the Budget

Long-running AI agent loops often fail not due to model errors or network issues, but because developers track only one cost dimension — typically compute spend — while ignoring wall clock time, context size, and disk usage. Attributing a mid-run failure to the last visible event is usually misleading, as the real cause is an unmanaged budget across multiple dimensions. Verifying agent work via transcript text or exit codes is unreliable; side effects like file hashes and live endpoints are the only trustworthy evidence that a step actually executed. Loops should be treated as event logs with timestamped, hashed steps rather than reproducible functions, since sampling variation and context drift mean no two runs are identical. When steps fail, naive instant retries compound costs and mask root causes — exponential backoff with a hard stop and journal-based resumption from the last good step is the recommended approach.