Assess whether agents must have a human gate for external actions (bbc2c7)

Situation

Multi-model reconciliation lead in a regulated entity that cannot reconstruct last month's decisions has one working extract — output-scoring rubric that never fails a high-risk output — after an examiner asking who authorized last Tuesday's model output. If output-scoring rubric that never fails a high-risk output cannot support agents must have a human gate, the honest AI Governance Layer output is hold.

Decision

Multi-model reconciliation lead in a regulated entity that cannot reconstruct last month's decisions 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 examiner asking who authorized last Tuesday's model output.

Hypotheses to test

• An examiner asking who authorized last Tuesday's model output is noise around an already-controlled Lifecycle and Accountability process in a regulated entity that cannot reconstruct last month's decisions, given output-scoring rubric that never fails a high-risk output.
• An examiner asking who authorized last Tuesday's model output is the event in output-scoring rubric that never fails a high-risk output that forces Policy or governance breach 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 an examiner asking who authorized last Tuesday's model output, not a Lifecycle and Accountability program failure.
• Output-scoring rubric that never fails a high-risk output cannot decide agents must have a human gate yet after an examiner asking who authorized last Tuesday's model output; hold is the only AI Governance Layer close a regulated entity that cannot reconstruct last month's decisions can defend.

Analysis required

• Test whether an examiner asking who authorized last Tuesday's model output 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 regulated entity that cannot reconstruct last month's decisions.
• For this AI Governance Layer Lifecycle and Accountability file, read output-scoring rubric that never fails a high-risk output against an examiner asking who authorized last Tuesday's model output and write the one fact that would move agents must have a human gate for multi-model reconciliation lead.