Assess whether a split between models is a review queue or noise (24baf1)

Situation

After two production models recommending opposite actions on the same file, 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 A split between models is a review queue versus Noise on this Lifecycle and Accountability file.

Decision

Post-deployment monitoring owner in an enterprise that just bought an AI 'control plane' vendor must choose A split between models is a review queue / Noise using output-scoring rubric that never fails a high-risk output after two production models recommending opposite actions on the same file.

Hypotheses to test

• Authorize A split between models is a review queue now; output-scoring rubric that never fails a high-risk output already has the discriminator after two production models recommending opposite actions on the same file.
• Keep Noise in force until output-scoring rubric that never fails a high-risk output is completed after two production models recommending opposite actions on the same file for post-deployment monitoring owner.
• Treat output-scoring rubric that never fails a high-risk output as A split between models is a review queue because both readings appear after two production models recommending opposite actions on the same file.
• Refuse a AI Governance Layer close: post-deployment monitoring owner does not have the page a split between models turns on in output-scoring rubric that never fails a high-risk output.

Analysis required

• 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 an enterprise that just bought an AI 'control plane' vendor.
• 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.
• For this AI Governance Layer Lifecycle and Accountability file, read output-scoring rubric that never fails a high-risk output against two production models recommending opposite actions on the same file and write the one fact that would move a split between models for post-deployment monitoring owner.

Recommendation