Assess whether a split between models is a review queue or noise (2b55d9)

Situation

In a hospital committee that never records dissent, output-scoring rubric that never fails a high-risk output is the evidence after a committee that has not met since the last incident. Enterprise AI control-plane owner has to pick A split between models is a review queue or Noise for this AI Governance Layer Lifecycle and Accountability close using output-scoring rubric that never fails a high-risk output.

Decision

Enterprise AI control-plane owner in a hospital committee that never records dissent must choose A split between models is a review queue / Noise using output-scoring rubric that never fails a high-risk output after a committee that has not met since the last incident.

Hypotheses to test

• A committee that has not met since the last incident 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.
• A committee that has not met since the last incident is the event in output-scoring rubric that never fails a high-risk output that forces A split between models is a review queue for enterprise AI control-plane owner under AI Governance Layer.
• Output-scoring rubric that never fails a high-risk output shows a one-file miss after a committee that has not met since the last incident, not a Lifecycle and Accountability program failure.
• Output-scoring rubric that never fails a high-risk output cannot decide a split between models yet after a committee that has not met since the last incident; hold is the only AI Governance Layer close a hospital committee that never records dissent can defend.

Analysis required

• Test whether a committee that has not met since the last incident changed routing, logging, or human-in-the-loop on the live agent path.
• Score whether the agent action in output-scoring rubric that never fails a high-risk output was in-policy, out-of-policy, or unlogged.
• Confirm the inventory line still matches the running configuration in a hospital committee that never records dissent.
• For this AI Governance Layer Lifecycle and Accountability file, read output-scoring rubric that never fails a high-risk output against a committee that has not met since the last incident and write the one fact that would move a split between models for enterprise AI control-plane owner.