Assess whether the control plane actually controls production traffic (3fac30)

SITUATION A regulated entity that cannot reconstruct last month's decisions has vendor MSA clauses on training, indemnity, and subprocessors in hand following a scorecard that rated 100% of outputs 'acceptable'. Multi-model reconciliation lead must determine whether the control plane actually controls production traffic for this AI Governance Layer Lifecycle and Accountability file.

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 vendor MSA clauses on training, indemnity, and subprocessors after a scorecard that rated 100% of outputs 'acceptable'. The question on that file is whether the control plane actually controls production traffic.

HYPOTHESES TO TEST 1. Authorize Policy or governance breach now; vendor MSA clauses on training, indemnity, and subprocessors already has the discriminator after a scorecard that rated 100% of outputs 'acceptable'. 2. Keep Model defect in force until vendor MSA clauses on training, indemnity, and subprocessors is completed after a scorecard that rated 100% of outputs 'acceptable' for multi-model reconciliation lead. 3. Treat vendor MSA clauses on training, indemnity, and subprocessors as Dual failure because both readings appear after a scorecard that rated 100% of outputs 'acceptable'. 4. Refuse a AI Governance Layer close: multi-model reconciliation lead does not have the decision the control plane actually turns on in vendor MSA clauses on training, indemnity, and subprocessors.

ANALYSIS REQUIRED 1. Test whether a scorecard that rated 100% of outputs 'acceptable' changed routing, logging, or human-in-the-loop on the live agent path. 2. Score whether the agent action in vendor MSA clauses on training, indemnity, and subprocessors was in-policy, out-of-policy, or unlogged. 3. Confirm the inventory line still matches the running configuration in a regulated entity that cannot reconstruct last month's decisions. 4. For this AI Governance Layer Lifecycle and Accountability file, read vendor MSA clauses on training, indemnity, and subprocessors against a scorecard that rated 100% of outputs 'acceptable' and write the one fact that would move the control plane actually for multi-model reconciliation lead.

RECOMMENDATION Choose Policy or governance breach / Model defect / Dual failure / Hold for the missing fact on this AI Governance Layer / Lifecycle and Accountability packet (vendor MSA clauses on training, indemnity, and subprocessors after a scorecard that rated 100% of outputs 'acceptable'). The follow-on Lifecycle and Accountability action is what multi-model reconciliation lead does next: implement the option, assign an owner, and log the missing fact.