Assess whether a split between models is a review queue or noise (ffb848)

Situation

Multi-model reconciliation lead in a publisher needing provenance on generated copy has one working extract — output-scoring rubric that never fails a high-risk output — after a batch job still calling a retired endpoint. If output-scoring rubric that never fails a high-risk output cannot support a split between models, the honest AI Governance Layer output is hold.

Decision

Multi-model reconciliation lead in a publisher needing provenance on generated copy must choose A split between models is a review queue / Noise using output-scoring rubric that never fails a high-risk output after a batch job still calling a retired endpoint.

Hypotheses to test

• A batch job still calling a retired endpoint is noise around an already-controlled Control Plane and Scoring process in a publisher needing provenance on generated copy, given output-scoring rubric that never fails a high-risk output.
• A batch job still calling a retired endpoint 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 multi-model reconciliation lead under AI Governance Layer.
• Output-scoring rubric that never fails a high-risk output shows a one-file miss after a batch job still calling a retired endpoint, not a Control Plane and Scoring program failure.
• Output-scoring rubric that never fails a high-risk output cannot decide a split between models yet after a batch job still calling a retired endpoint; hold is the only AI Governance Layer close a publisher needing provenance on generated copy can defend.

Analysis required

• 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.
• Confirm the inventory line still matches the running configuration in a publisher needing provenance on generated copy.
• 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 split between models for multi-model reconciliation lead.

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