“The agent handled 500 tasks” describes activity, not success. Those tasks could be wrong, require extensive repair, or represent work that nobody needed. To know whether an AI deployment is useful, measure the outcome that motivated it and the controls that keep it safe. Evaluation begins before launch with a baseline and a clear question; it continues after launch through sampling, incident review, and decisions to adjust, limit, or stop the workflow.
Choose a metric that reflects the job
Write one sentence describing the purpose of the workflow, such as “prepare a complete first draft of routine service replies for staff review.” Then choose measures that correspond to that purpose. Possible process measures include eligible items handled, completion rate, time to human review, correction frequency, and successful handoff. Quality measures may include factual accuracy against an approved source, completeness, policy adherence, correct routing, and whether the tone is appropriate. Safety measures should capture privacy issues, unauthorized actions, wrong-recipient events, and failures to escalate.
Do not combine everything into a single score unless the weighting is explicit and defensible. A high average may hide a small number of severe errors. Report counts as well as rates and give denominators: three corrections among 20 reviewed items mean something different from three among 2,000. Segment by task type and risk where possible, but avoid collecting sensitive data that is not necessary.
Set a baseline and comparison method
Before the system is used, sample the current process using the same definitions intended for the pilot. Record handling time, review time, exception rate, delay, and quality checks. Note the period, team, and workload conditions. If volumes fluctuate across seasons or campaigns, a short baseline may not represent normal operations. When a parallel comparison is practical, keep a similar group using the old process or have a reviewer assess outputs without knowing which method created them. Be careful not to treat an uncontrolled before-and-after change as proof of causation.
Define the sample plan in advance. Review a random selection of ordinary cases and deliberately include unusual or high-risk cases. The ordinary sample estimates routine behavior; edge-case testing checks whether controls hold. Record every serious incident rather than letting rare failures disappear inside a percentage. Explain the limitations of small samples and avoid thresholds that imply statistical certainty without a suitable design.
Review the whole workflow, not just the model output
Trace each item from its source to its final result. Was the input complete? Did the system use the approved source? Was a person asked to approve when required? Did the approved action actually occur in the destination system? A correct draft sent to the wrong person is still a failure. A quick response that triggers a second contact because it did not answer the question may not be an improvement. Check delivery, record updates, and exception closure, not just text quality.
Ask reviewers to label errors consistently: missing context, unsupported claim, wrong classification, outdated reference, tone issue, permission error, or workflow failure. Capture severity and likely cause. A corrective action should target the source of the problem. If the policy is ambiguous, revise policy; if data is stale, improve the source; if a prompt has a gap, add an instruction and regression test. Prompt changes alone cannot repair every failure.
Establish a recurring review and ownership
Assign an owner who can read the logs, gather staff feedback, and pause the workflow. Hold a short recurring review that covers volume, quality sample, escalations, incidents, system failures, and changes in inputs or policy. Keep decisions and configuration versions so the team can explain why behavior changed. Train staff to report near misses without fear of blame; otherwise the most important evidence may never reach the owner.
Review access and data handling alongside performance. A system may perform accurately while retaining more information than intended or exposing results to an overly broad audience. Confirm the approved purpose, permissions, retention schedule, and deletion route. Ask the provider for evidence about its specific controls instead of assuming a general security statement answers your requirements.
Decide whether to expand, recalibrate, or stop
Set decision criteria before the pilot: what level of quality is acceptable for this task, which failures require immediate pause, how much review effort is sustainable, and what evidence is needed before scope expands. Thresholds should reflect risk and be approved by the workflow owner, not copied from another company. For instance, a draft-only internal summary has a different risk profile than an automated financial action. No single accuracy percentage makes every use safe.
When performance worsens, examine input mix, source changes, staffing, configuration updates, and system outages. A decline might reflect new cases rather than model drift. Pause the affected path if the cause is unclear or the potential harm is high. Notify impacted people and correct records when necessary. Resume only after a targeted test demonstrates the fix and an owner signs off.
Pair this practice with source-grounded weekly reporting and a cautious approach to operational alerts. Review the pricing page and verify available logs, controls, and review processes in the actual service agreement.
Performance measurement is not a campaign to prove a purchase was right. It is a way to find out whether the system earns trust in a specific job. Publish limits beside results, keep human responsibility clear, and let evidence—not enthusiasm—determine the next step.
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