What should buyers inspect before trusting a generated evaluation pack?

Inspect the generated material and its supporting explanation before using the pack to compare agents or guide training.

- Ask which business mechanisms informed generation and how they were translated. - Review grounding and disclosure risk; generated output does not guarantee privacy compliance. - Check that identifiers, wording or filenames do not reveal expected outcomes. - Inspect whether visible policies contain all rules required for a valid decision. - Review whether variations change meaningful conditions rather than only surface wording. - Separate training material from evaluation cases by underlying scenario structure. - Have a reviewer independently challenge expected outcomes and evaluator logic.

Business plausibility and evaluator correctness are separate concerns. A convincing request can still have an incorrect expected result. Conversely, a technically consistent state machine can rely on an implausible authority rule that teaches the wrong operational behaviour.

MirrorFirm could provide generated records with a description of their intended mechanisms and boundaries for buyer inspection. Public distribution deserves particular care because prompts, exports and server responses may expose clues that were intended to remain evaluator-only. The review should cover every representation that a tested agent can access, not just the primary dataset file.