Assess whether the control plane actually controls production traffic (f531ed)

SITUATION Enterprise AI control-plane owner is responsible for the control plane actually in a hospital committee that never records dissent, using output-scoring rubric that never fails a high-risk output as the only working extract. A scorecard that rated 100% of outputs 'acceptable' is what reset the timeline for this AI Governance Layer Lifecycle and Accountability file.

DECISION Enterprise AI control-plane owner in a hospital committee that never records dissent 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 a scorecard that rated 100% of outputs 'acceptable'.

HYPOTHESES TO TEST 1. Output-scoring rubric that never fails a high-risk output reads as Policy or governance breach once a scorecard that rated 100% of outputs 'acceptable' 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 a scorecard that rated 100% of outputs 'acceptable'; 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 enterprise AI control-plane owner in a hospital committee that never records dissent. 4. Output-scoring rubric that never fails a high-risk output is missing the fact enterprise AI control-plane owner needs after a scorecard that rated 100% of outputs 'acceptable'; stop this AI Governance Layer close.

ANALYSIS REQUIRED 1. Name the override that would let the control plane actually proceed without a silent bypass. 2. Test whether a scorecard that rated 100% of outputs 'acceptable' changed routing, logging, or human-in-the-loop on the live agent path. 3. Score whether the agent action in output-scoring rubric that never fails a high-risk output was in-policy, out-of-policy, or unlogged. 4. For this AI Governance Layer Lifecycle and Accountability file, read output-scoring rubric that never fails a high-risk output against a scorecard that rated 100% of outputs 'acceptable' and write the one fact that would move the control plane actually for enterprise AI control-plane 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 a scorecard that rated 100% of outputs 'acceptable'). The follow-on Lifecycle and Accountability action is what enterprise AI control-plane owner does next: implement the option, assign an owner, and log the missing fact.