A polished demonstration shows that an AI feature can work on a selected example. An evaluation asks whether it works consistently on the tasks people will actually give it. That distinction matters before a team connects the feature to a shared inbox, knowledge base, or business workflow.
Define a successful task in operational terms
For a support-drafting pilot, success might mean identifying the right product, using the current support policy, and producing a draft that an agent can verify quickly. A fluent paragraph alone is not enough. Write down which mistakes make the draft unusable and which can be corrected with a minor edit.
Create a reviewed set of representative, appropriately sanitized requests. Include incomplete questions, contradictory information, and situations where escalation is the correct result. Keep some examples separate from the material used to tune the system.
Inspect both results and behavior
Anthropic’s evaluation guidance distinguishes task outcomes from the steps an agent takes to reach them. Apply that distinction by checking the final draft as well as retrieved evidence and any tool actions. A plausible answer reached through unauthorized data access is a failure.
Combine objective checks, such as required fields and permitted actions, with a clear rubric for human review. Repeat uncertain cases to expose variability. If a model grades responses, compare its judgments with reviewers before treating its score as reliable.
Set the expansion decision before the demo
Agree which failures block rollout and who owns the decision. Compare review effort, completion time, and error severity with the existing process. Preserve the evaluation inputs and system version so later prompt, model, or retrieval changes can be compared. Expand access in a controlled group, keep a route back to the previous workflow, and add production failures to the evaluation set.
