Whether a score that never fails is a control or theater from output-scoring

Situation

After a batch job still calling a retired endpoint, output-scoring rubric that never fails a high-risk output is what multi-model reconciliation lead can touch in a publisher needing provenance on generated copy. AI Governance Layer will live with A score that never fails is a control versus Theater on this Control Plane and Scoring file.

Decision

Multi-model reconciliation lead in a publisher needing provenance on generated copy must choose A score that never fails is a control / Theater using output-scoring rubric that never fails a high-risk output after a batch job still calling a retired endpoint.

Hypotheses to test

• Output-scoring rubric that never fails a high-risk output reads as A score that never fails is a control once a batch job still calling a retired endpoint is lined up to the same AI Governance Layer population.
• Output-scoring rubric that never fails a high-risk output is closer to Theater after a batch job still calling a retired endpoint; A score that never fails is a control would over-claim this Control Plane and Scoring extract.
• A dual reading is still live in output-scoring rubric that never fails a high-risk output for multi-model reconciliation lead in a publisher needing provenance on generated copy.
• Output-scoring rubric that never fails a high-risk output is missing the fact multi-model reconciliation lead needs after a batch job still calling a retired endpoint; stop this AI Governance Layer close.

Analysis required

• Name the override that would let a score that never proceed without a silent bypass.
• Test whether a batch job still calling a retired endpoint 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.
• For this AI Governance Layer Control Plane and Scoring file, read output-scoring rubric that never fails a high-risk output against a batch job still calling a retired endpoint and write the one fact that would move a score that never for multi-model reconciliation lead.

Recommendation

Choose A score that never fails is a control / Theater on this AI Governance Layer / Control Plane and Scoring packet (output-scoring rubric that never fails a high-risk output after a batch job still calling a retired endpoint). The follow-on Control Plane and Scoring action is what multi-model reconciliation lead does next: implement the option, assign an owner, and log the missing fact.