Assess whether the committee can overrule a business unit (64a09f)

SITUATION After an agent that refunded customers above its limit, output-scoring rubric that never fails a high-risk output is what post-deployment monitoring owner can touch in an enterprise that just bought an AI 'control plane' vendor. AI Governance Layer will live with Policy or governance breach versus Model defect on this Lifecycle and Accountability file.

DECISION Post-deployment monitoring owner in an enterprise that just bought an AI 'control plane' vendor must choose Policy or governance breach / Model defect / Dual failure / Hold for the missing fact using output-scoring rubric that never fails a high-risk output after an agent that refunded customers above its limit.

HYPOTHESES TO TEST 1. Output-scoring rubric that never fails a high-risk output reads as Policy or governance breach once an agent that refunded customers above its limit is lined up to the same AI Governance Layer population. 2. Output-scoring rubric that never fails a high-risk output is closer to Model defect after an agent that refunded customers above its limit; Policy or governance breach would over-claim this Lifecycle and Accountability extract. 3. Dual failure is still live in output-scoring rubric that never fails a high-risk output for post-deployment monitoring owner in an enterprise that just bought an AI 'control plane' vendor. 4. Output-scoring rubric that never fails a high-risk output is missing the fact post-deployment monitoring owner needs after an agent that refunded customers above its limit; stop this AI Governance Layer close.

ANALYSIS REQUIRED 1. Score whether the agent action in output-scoring rubric that never fails a high-risk output was in-policy, out-of-policy, or unlogged. 2. Confirm the inventory line still matches the running configuration in an enterprise that just bought an AI 'control plane' vendor. 3. Map the control-plane score in output-scoring rubric that never fails a high-risk output to the policy gate post-deployment monitoring owner can enforce. 4. For this AI Governance Layer Lifecycle and Accountability file, read output-scoring rubric that never fails a high-risk output against an agent that refunded customers above its limit and write the one fact that would move the committee can overrule for post-deployment monitoring owner.

RECOMMENDATION Choose Policy or governance breach / Model defect / Dual failure / Hold for the missing fact on this AI Governance Layer / Lifecycle and Accountability packet (output-scoring rubric that never fails a high-risk output after an agent that refunded customers above its limit). If output-scoring rubric that never fails a high-risk output cannot force a AI Governance Layer label under Lifecycle and Accountability, stop. If output-scoring rubric that never fails a high-risk output after an agent that refunded customers above its limit cannot support Policy or governance breach versus Model defect on this AI Governance Layer Lifecycle and Accountability close, post-deployment monitoring owner must leave the classification unresolved and name the missing control or provenance fact.