Evaluating AI agents: measure the task, trajectory and risk

Define success at task level

Do not measure whether an answer sounds convincing; measure whether the task finished correctly within rules and time. Record required outcomes, prohibited actions and acceptable uncertainty for each scenario.

Collect representative cases

Include routine tasks, difficult edges, incomplete input and known failures. Remove sensitive data or use controlled synthetic variants. Hold a separate set back for regression testing.

Assess outcome and trajectory

A correct final answer can arise through unauthorised data or needless actions. Inspect tool choices, consent, source use, recovery behaviour and stopping point alongside final output.

Combine graders

Exact rules work well for schemas and prohibited actions. Model grading helps with open text but needs calibration against human examples. Use people where context, brand voice or impact carries weight.

There is no universal number. Start with critical task variations and add cases until new failure types become uncommon.

Compare with a baseline

Run a new prompt, tool or model beside the existing approach on the same set. Report uncertainty and segments; an average can hide severe failure in one task type.

Make evaluation operational

Run a fast core set before release, a broader set periodically and production checks on logged technical signals. Turn every confirmed incident into a new regression case and assign ownership to failing categories.

{"input":"sample task","expected":{"outcome":"reviewable result"},"checks":["correctness","tool-use","safe-stop"]}

Make AI agent evaluations verifiable

The decision review for AI agent evaluations first focuses on a representative task set with successes, safe failures, edge cases and evidence of tool use. Define the acceptable user outcome, essential inputs and authorised approver for AI agent evaluations before the trial starts. Keep the first AI agent evaluations trial small enough to separate causes from effects.

Build evidence for AI agent evaluations around one realistic successful route and also simulate a single happy-path score, model or prompt drift and a grader that confuses style with correctness. For every AI agent evaluations check, record the expected outcome, visible evidence and recovery action when it fails. The final decision question for AI agent evaluations is: “How many evaluation cases are enough?”

AI agent evaluations: from trial to everyday operation

Assign AI agent evaluations one operational owner, one subject reviewer and a clear fallback route. Treat the AI agent evaluations checklist as separate evidenced steps, so punctuation or phrasing never becomes part of the process logic. Ask the relevant user to complete AI agent evaluations without spoken help and record every point that still needs explanation or manual recovery.

Keep change rights, logging, support and review dates for AI agent evaluations in one operating plan. Repeat the AI agent evaluations trial after a change to source data, configuration, model, integration or user role. Expand AI agent evaluations only when the team can also detect, contain and recover a single happy-path score, model or prompt drift and a grader that confuses style with correctness.

Stop the AI agent evaluations rollout while a single happy-path score, model or prompt drift and a grader that confuses style with correctness is not reported visibly and recoverable by the assigned owner.