◇ Could this help me?
Yes. All five are standard, well-chosen evaluation dimensions, and the central claim holds: a single accuracy number hides whether the errors land on the cases that matter, what the agent costs to run, and whether it knows when to hand off. Tracking hallucination rate per topic rather than in aggregate is the sharpest specific idea here, because it converts a vague trust problem into a scoped one. General practice, with one caveat the video skips: these numbers are only comparable against a fixed evaluation set, otherwise they drift with whatever traffic arrived that week.
Help me build an evaluation scorecard for an AI agent that goes beyond a single accuracy number. For my use case, define how to measure task completion rate, faithfulness to source material, hallucination rate broken down per topic, cost per task in tokens and API and tool calls, and human escalation rate. For each one, tell me what to log, how to compute it, and what a healthy range looks like. Propose a fixed evaluation set so the numbers stay comparable over time, then identify which of these my current setup cannot measure at all and what it would take to instrument it.