Assess whether disagreement should block, queue, or log (d34e92)

SITUATION Enterprise AI control-plane owner in a hospital committee that never records dissent has one working extract — output-scoring rubric that never fails a high-risk output — after an examiner asking who authorized last Tuesday's model output. If output-scoring rubric that never fails a high-risk output cannot support disagreement should block, queue,, the only defensible AI Governance Layer output is hold.

DECISION Enterprise AI control-plane owner in a hospital committee that never records dissent must choose Disagreement should block, queue, / Log using output-scoring rubric that never fails a high-risk output after an examiner asking who authorized last Tuesday's model output.

HYPOTHESES TO TEST 1. An examiner asking who authorized last Tuesday's model output is noise around an already-controlled Lifecycle and Accountability process in a hospital committee that never records dissent, given output-scoring rubric that never fails a high-risk output. 2. An examiner asking who authorized last Tuesday's model output is the event in output-scoring rubric that never fails a high-risk output that forces Disagreement should block, queue, for enterprise AI control-plane owner under AI Governance Layer. 3. Output-scoring rubric that never fails a high-risk output shows a one-file miss after an examiner asking who authorized last Tuesday's model output, not a Lifecycle and Accountability program failure. 4. Output-scoring rubric that never fails a high-risk output cannot decide disagreement should block, queue, yet after an examiner asking who authorized last Tuesday's model output; hold is the only AI Governance Layer close a hospital committee that never records dissent can defend.

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 a hospital committee that never records dissent. 3. Map the control-plane score in output-scoring rubric that never fails a high-risk output to the policy gate enterprise AI control-plane 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 examiner asking who authorized last Tuesday's model output and write the one fact that would move disagreement should block, queue, for enterprise AI control-plane owner.

RECOMMENDATION Choose Disagreement should block, queue, / Log on this AI Governance Layer / Lifecycle and Accountability packet (output-scoring rubric that never fails a high-risk output after an examiner asking who authorized last Tuesday's model output). 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.