Assess whether the control plane actually controls production traffic (79ca4e)

SITUATION An enterprise that just bought an AI 'control plane' vendor has output-scoring rubric that never fails a high-risk output in hand following an agent that refunded customers above its limit. Post-deployment monitoring owner must determine whether the control plane actually controls production traffic for this AI Governance Layer Lifecycle and Accountability file.

DECISION Post-deployment monitoring owner in an enterprise that just bought an AI 'control plane' vendor must choose Policy or governance breach / Model defect / Dual failure / Hold for the missing fact using output-scoring rubric that never fails a high-risk output after an agent that refunded customers above its limit.

HYPOTHESES TO TEST 1. The population in output-scoring rubric that never fails a high-risk output is the one an agent that refunded customers above its limit named, so Policy or governance breach follows for this Lifecycle and Accountability file. 2. The population in output-scoring rubric that never fails a high-risk output is adjacent only to an agent that refunded customers above its limit; Model defect is the honest AI Governance Layer call. 3. An enterprise that just bought an AI 'control plane' vendor already contained an agent that refunded customers above its limit before output-scoring rubric that never fails a high-risk output arrived; no new Lifecycle and Accountability path. 4. Provenance on output-scoring rubric that never fails a high-risk output after an agent that refunded customers above its limit is broken; do not pick Policy or governance breach or Model defect yet.

ANALYSIS REQUIRED 1. Confirm the inventory line still matches the running configuration in an enterprise that just bought an AI 'control plane' vendor. 2. 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. 3. Name the override that would let the control plane actually proceed without a silent bypass. 4. For this AI Governance Layer Lifecycle and Accountability file, read output-scoring rubric that never fails a high-risk output against an agent that refunded customers above its limit and write the one fact that would move the control plane actually for post-deployment monitoring owner.

RECOMMENDATION Choose Policy or governance breach / Model defect / Dual failure / Hold for the missing fact on this AI Governance Layer / Lifecycle and Accountability packet (output-scoring rubric that never fails a high-risk output after an agent that refunded customers above its limit). Lead with the AI Governance Layer option output-scoring rubric that never fails a high-risk output can support after an agent that refunded customers above its limit, then the two facts that force it, then the Monday action for post-deployment monitoring owner in an enterprise that just bought an AI 'control plane' vendor.