How are you all actually evaluating agent decisions, not just agent outputs?

Reddit r/AI_Agents News

Summary

The author questions how to evaluate AI agent decisions beyond simple pass/fail metrics, proposing cost-weighted evaluations and confidence calibration.

Most agent eval I see (DeepEval, faithfulness scoring, etc) checks whether the OUTPUT is good — is it faithful, did it resist a prompt injection, etc. Pass/fail. But I've been building an agent that makes an actual decision with a cost attached (pay a supplier / verify / escalate), and pass/fail feels way too blunt. A wrong "pay" that loses money and a wrong "escalate" that just wastes 10 minutes are both "fail" but wildly different in reality. Curious what people here do. Do you attach real cost weights to different failure types? Check whether the agent's confidence is calibrated? Or is it mostly still "looks right in the demo, ship it"? Genuinely asking because I might be overcomplicating this.
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