What should an agent save so a failed run can be replayed safely?

Reddit r/AI_Agents News

Summary

The article discusses what data agents should save for safe replay of failed runs, distinguishing between reproducing failures and resuming after fixes, while balancing automatic retries with human intervention.

A failure trace helps explain why an agent failed. Recovering safely takes more context. As I’m implementing recovery for agent workflows, I’m separating two scenarios: • Reproduce the failure: preserve the inputs, prompt and tool versions, tool responses, and approval decisions. • Resume after a fix: recover from a safe checkpoint and check which external actions have already happened. A retry shouldn’t send the same message twice or create a duplicate record. Tracking external IDs and checking for completed actions are part of how I’m handling this. I’m also balancing recovery with retention: keeping enough context to understand decisions without storing every sensitive payload. That record also shapes the operational response when a dependency fails: retry automatically from the checkpoint, notify someone with the relevant failure trace, or wait for a person to fix the issue and approve the rerun. What’s the minimum an agent needs to save to make an automatic retry safe? And when should a person step in?
Original Article

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