When your agent screws up in production, how do you figure out which step went wrong?

Reddit r/AI_Agents Tools

Summary

A developer shares the challenge of debugging multi-step agents in production, where failures are hard to trace due to complex tool use and confident wrong answers, and asks the community for better monitoring and regression detection approaches.

Been building multi-step agents and the thing that's killing me isn't building them, it's knowing what happened when they fail. Like the agent works fine when I test it, then in real use it does something dumb — picks the wrong tool, or gives a confident wrong answer — and I'm stuck digging through logs trying to figure out which step in the chain actually went off the rails. Right now my "process" is honestly just print statements everywhere and re-reading the trace by hand. Feels primitive. How are you all handling this? * Do you have any real way to catch when an agent regresses after you change something? * For the people running agents in prod — how do you even know they're still working well day to day? * Anyone found something that actually helps here or is everyone just reading logs? Trying to figure out if I'm doing this the hard way or if there just isn't a good answer yet
Original Article

Similar Articles

How do you actually debug your AI agents?

Reddit r/AI_Agents

Developer shares struggles debugging AI agents in production, highlighting issues with hallucinations, regression from prompt changes, and high API costs, asking the community for strategies.

Agent failure clusters changed how I think about debugging

Reddit r/AI_Agents

A developer shares how visualizing failure clusters across many agent runs changed their debugging approach, emphasizing the need for a feedback loop so agents learn from past mistakes rather than treating failures as isolated bugs. The post highlights manual workarounds and a platform called BentoLabs that implements closed-loop improvement.

I analyzed how 50+ AI teams debug production agent failures and got surprised

Reddit r/AI_Agents

Based on interviews with 50+ AI teams, the author highlights that production agent failures often stem from minor prompt or configuration issues rather than deep model problems. The article advocates for adopting software engineering practices like versioning, A/B testing, and experiment tracking to improve reliability.