Assess whether disagreement should block, queue, or log (e29a2e)

SITUATION Content-attribution program lead in a firm whose vendor MSA is silent on training rights 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 disagreement should block, queue,, the only defensible AI Governance Layer output is hold.

DECISION Content-attribution program lead in a firm whose vendor MSA is silent on training rights must choose Disagreement should block, queue, / Log using output-scoring rubric that never fails a high-risk output after a batch job still calling a retired endpoint.

HYPOTHESES TO TEST 1. Authorize Disagreement should block, queue, now; output-scoring rubric that never fails a high-risk output already has the discriminator after a batch job still calling a retired endpoint. 2. Keep Log in force until output-scoring rubric that never fails a high-risk output is completed after a batch job still calling a retired endpoint for content-attribution program lead. 3. Treat output-scoring rubric that never fails a high-risk output as Disagreement should block, queue, because both readings appear after a batch job still calling a retired endpoint. 4. Refuse a AI Governance Layer close: content-attribution program lead does not have the decision disagreement should block, queue, turns on in output-scoring rubric that never fails a high-risk output.

ANALYSIS REQUIRED 1. Map the control-plane score in output-scoring rubric that never fails a high-risk output to the policy gate content-attribution program lead can enforce. 2. Name the override that would let disagreement should block, queue, proceed without a silent bypass. 3. Test whether a batch job still calling a retired endpoint changed routing, logging, or human-in-the-loop on the live agent path. 4. For this AI Governance Layer Lifecycle and Accountability 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 disagreement should block, queue, for content-attribution program lead.

RECOMMENDATION Choose Disagreement should block, queue, / Log on this AI Governance Layer / Lifecycle and Accountability packet (output-scoring rubric that never fails a high-risk output after a batch job still calling a retired endpoint). Lead with the AI Governance Layer option output-scoring rubric that never fails a high-risk output can support after a batch job still calling a retired endpoint, then the two facts that force it, then the Monday action for content-attribution program lead in a firm whose vendor MSA is silent on training rights.