AI Agent Evaluation Metrics, Scorecards and Release Thresholds
An agent scorecard should separate capability, control, reliability and business performance, preserve critical failures and make the release decision explicit.
Begin with the decision the scorecard supports
State whether the evaluation chooses between versions, authorises a pilot, expands autonomy or monitors production. The same evidence may support restricted use and fail to support full automation.
Measure understanding and task completion
Track intent resolution, complete task success, correct routing, grounded response and whether the user or downstream process received the required result. Define the denominator and completion evidence.
Measure action and authority
Report correct tool selection, valid inputs, successful execution, verified completion, permission breaches, missed approval and unnecessary escalation. A correct narrative does not cancel an invalid action.
Measure memory, handover and recovery
Track lost constraints, unsupported recall, incomplete handover, duplicate work, unresolved commitments, retry safety and recovery after tool or system failure.
Measure business performance
Compare cycle time, effort, correction, customer outcome, risk exposure, cost and capacity with the existing process and a simpler design. Report model, tool, monitoring and human review costs.
Use gates and weighted scores carefully
A weighted score can summarise broad performance, but critical privacy, safety and authority failures should remain explicit release blockers. Publish the weights, thresholds, denominators and uncertainty. Do not claim that a synthetic scenario mix estimates production prevalence unless it was sampled for that purpose.
Segment the results
Break results down by workflow route, case difficulty, language, channel, customer or asset group, tool and consequence. One average can hide a weak route or subgroup that determines the real operating risk.