Define a successful job before testing
An agent can produce a polished response while missing the business task entirely. Before introducing one, write a short description of a successful outcome. For a service request agent, that might mean finding the correct account, recognizing the issue, preparing an accurate response, and sending unusual cases to the right person.
Make the definition concrete enough that two team members would reach a similar judgment. Include the information the agent must use, actions it may take, and situations that require approval. This turns an interesting demonstration into work you can evaluate consistently.
Test the ordinary work and the awkward work
Build a small test collection from representative requests with private details removed or appropriately protected. Include incomplete forms, contradictory instructions, outdated documents, and customers who ask for something outside the service. A collection made only of easy examples gives an incomplete picture of readiness.
For each case, record the expected result and why it is appropriate. Ask a knowledgeable employee to review the outcome without relying on the agent's explanation of its own performance. NIST's AI Risk Management Framework supports testing before deployment and continued evaluation during operation; your test collection makes that principle practical.
Measure the work people still have to do
Track the share of tasks completed correctly, the corrections required, and the time a reviewer spends checking them. Add a separate measure for appropriate escalation. An agent that stops and asks for help on an ambiguous request may be performing better than one that confidently finishes with the wrong answer.
In a hypothetical scheduling workflow, a draft that takes five minutes to repair may offer little benefit over a familiar manual process. Compare the complete workflow, including review and exception handling. Count avoided rework and reduced waiting only when your observations support those improvements.
Make failures useful and visible
Group problems by cause: missing information, an unclear instruction, a faulty connection, an unsuitable action, or a misunderstood business rule. These categories point toward different fixes. Rewriting instructions will not repair an expired system connection, and adding more data will not clarify who can approve a refund.
Keep a simple record of what happened, its impact, and the corrective action. Avoid hiding serious mistakes inside an average score. A system can achieve a strong completion rate while still making a small number of unacceptable decisions. Give those outcomes their own review and a clear stopping rule.
Treat launch as the start of measurement
Begin with a limited workflow and review a meaningful sample of results regularly. Repeat your tests when instructions, connected systems, or source documents change. New products, seasonal requests, and new staff can all change the work the agent encounters. A result from last month's pilot does not answer every question about next month's use.
Assign an owner who can pause the agent, investigate feedback, and approve improvements. Give employees an easy way to flag an outcome that looks wrong. Evaluation becomes valuable when it changes decisions about scope, oversight, and continued investment.
Choose one workflow, write a clear success standard, and compare complete outcomes and review effort before expanding the agent's responsibilities.
Sources & further reading
Primary references for the concepts discussed here. Any business examples in this article are illustrative; results depend on your process, tools, and implementation.