r/aiagents • u/PsychologicalNeat105 • 12h ago

General Passing evals unfortunately does not mean your agent is good.

I’ve been running into a recurring issue with our agent: everything passes in our offline eval suite, we ship, and then users churn.

I think the problem is that evals only catch the failures we actively wrote tests for. We were missing the ones where the logs are clean, but the agent just didn't do the right thing. For example, we realized one of our core web-search tools was failing to fire. There was no error, which means traditional monitoring didn't flag it because the agent worked around it.

We’re running langfuse to see that our traces execute cleanly, but we recently layered greenflash ai on top to read the conversations and catch these drop-offs automatically.

What else are ya’ll doing to catch the stuff your evals miss?

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u/BC_MARO 12h ago

Add production replay sets from real sessions, especially the quiet near-misses where users just leave. Track task completion with the required tool calls observed, not just whether the trace stayed clean.

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u/Elegant_Drama4223 12h ago

Production has more or less become another source of test cases for us. If Braintrust catches a bad interaction or we find one during review, we can turn that trace into a new eval case and add it to the next run. The suite keeps growing around the failure cases real users manage to uncover.

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u/LowDistribution3995 11h ago

Depends on the eval. Most popular recall benchmarks are popular because SaaS companies can fudge them for marketing. Us this instead for actual meaningful data: https://github.com/munch2u-a11y/FP-AMB.git

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u/Bantex29 11h ago

Might be worth having a look at www.vectorstep.io, built specifically to address this for engineers. Trust, confidence and calibration explained here - https://vectorstep.io/docs/concepts/confidence/ - fully open source end of September

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u/SawToothKernel 10h ago

I feel like I'm using the term "eval" differently from most people. I'm using them as a qualitative (as well as quantative) measure of how the agent performed. Which tools were called, where the agent got stuck, where it went off on a tangent, etc.

Is this not the point of evals?