What should buyers check in generated content before trusting agent scores?

Inspect the generated material before interpreting scores, because an agent can appear successful or unsuccessful for reasons caused by defects in the test world.

- Check referential integrity across requests, policies and queues, including records reached only through exception paths that a routine demonstration might never exercise. - Inspect temporal consistency so requests do not depend on evidence created later, unless the task explicitly includes waiting for that evidence to arrive. - Search agent-visible fields for answer leakage, including destination hints in filenames, hidden rationale copied into descriptions or identifiers that encode the expected class. - Review language variation for operational meaning, ensuring unusual wording introduces realistic ambiguity rather than arbitrary puzzles with no corresponding business decision to make. - Inspect how business grounding influenced generated content and assess disclosure risk; generated output alone does not establish anonymity or satisfy every intended use requirement. - Check dataset partitions for shared scenario templates and near-identical histories that could make evaluation reward familiarity instead of transferable routing behaviour. - Review disputed cases with the relevant policy evidence available, recording whether the defect belongs to task construction, evaluator logic or the agent’s actual decision.