Multi-model reconciliation lead must resolve whether disagreement should

Situation

After two production models recommending opposite actions on the same file, output-scoring rubric that never fails a high-risk output is what multi-model reconciliation lead can touch in an enterprise that just bought an AI 'control plane' vendor. AI Governance Layer will live with Disagreement should block, queue, versus Log on this Audit and Vendor Terms file.

Decision

Multi-model reconciliation lead in an enterprise that just bought an AI 'control plane' vendor must choose Disagreement should block, queue, / Log 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

• Two production models recommending opposite actions on the same file is noise around an already-controlled Audit and Vendor Terms process in an enterprise that just bought an AI 'control plane' vendor, given output-scoring rubric that never fails a high-risk output.
• Two production models recommending opposite actions on the same file is the event in output-scoring rubric that never fails a high-risk output that forces Disagreement should block, 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 two production models recommending opposite actions on the same file, not a Audit and Vendor Terms program failure.
• Output-scoring rubric that never fails a high-risk output cannot decide disagreement should block, queue, yet after two production models recommending opposite actions on the same file; hold is the only AI Governance Layer close an enterprise that just bought an AI 'control plane' vendor can defend.

Analysis required

• Test whether two production models recommending opposite actions on the same file 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 an enterprise that just bought an AI 'control plane' vendor.
• For this AI Governance Layer Audit and Vendor Terms 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 disagreement should block, queue, for multi-model reconciliation lead.