When AI assists with data governance, require agents to show their reasoning with citations, let humans review before committing changes, and record what humans actually verified—this prevents errors from propagating while capturing institutional knowledge.
This paper presents a system where AI agents propose metadata decisions for industrial data (like equipment classifications) with cited evidence, but humans review and approve each decision before it's recorded. The system keeps a full audit trail of what was checked, changed, and why—preventing AI errors from silently spreading through business systems while documenting human expertise.