My pipeline ran "successfully" for a week. Turned out my agent had been silently skipping failed API calls the whole time.

Reddit r/AI_Agents Tools

Summary

A developer recounts how their automated pipeline silently skipped failed API calls due to rate limiting, producing seemingly successful runs with empty data. They discuss the trade-off between retrying and hard-failing, and ask the community for best practices in agent error handling.

Built a multi-step automation that pulls data, runs some processing, and writes outputs. Ran it daily for about 8 days, green checkmarks every time. Then I spot-checked one of the outputs and something looked off. Dug into the logs and found that one of the downstream APIs had been rate-limiting responses since day 2. My agent was catching the 429s internally, logging nothing useful, and just continuing the pipeline with empty data in that slot. Final outputs looked structurally complete so nothing downstream flagged it. The fix was obvious in hindsight: hard fail on any non-2xx instead of silently filling with defaults. But I'm realizing I don't have a good mental model for where to draw the line between "retry and continue" vs "halt and alert." Right now I'm leaning toward treating any third-party API call as a potential silent failure point and adding explicit output validation after each one, but that gets verbose fast in a longer pipeline. Curious how others handle this. Do you let the agent decide when to retry vs escalate, or do you set hard rules outside the agent's control? And has anyone had a case where the agent's own retry logic made things worse?
Original Article

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