Build an evaluation set for regional OpenAI agents

Define the accepted result for each case

Write what a correct completion means before running the system. Some cases should end with a draft, some with a request for clarification, and some with refusal to act. For a Tennessee rollout, include examples from more than one operating context when the pilot scope covers several locations. Keep the original inputs so later changes can be compared against the same evidence.

Separate quality dimensions

Track source correctness, field accuracy, permitted actions, and reviewer effort independently. A single score can hide a serious action error behind many harmless formatting successes. NIST’s risk framework is a reference for measuring in context; our recommendation is to let the business owner define which failures block release and which are acceptable limitations for the tested scope.

Reference: NIST: AI Risk Management Framework

Test the tools around the model

Include a missing record, a slow dependency, a repeated event, and a denied operation. Confirm that the application preserves work and returns a useful next step. The model may respond appropriately while the integration still loses a pending case. Evaluation should inspect the resulting business records and the reviewer’s experience, not only the text returned by the model.

Maintain a regression set after launch

Add observed failures as cases with expected behavior. Keep a holdout set where useful so improvements are not judged only against examples repeatedly tuned during development. Agentix can connect evaluation to release and support. The practical handover should show which behavior was tested, what remains uncertain, and which changes require the relevant cases to run again.

Reference: Agentix (publisher): Agentix services

Common questions