Agentic AI systems increasingly combine model reasoning, tools, memory, orchestration, external services, and execution privileges. This creates a governance problem that is not solved by model capability alone: an AI system may reason, recommend, classify, or simulate, but those outputs should not automatically acquire authority to cause operational consequences. This article presents a governance-verification baseline for the Coherence Nexus (CNX) Framework, an authority-separated execution architecture in which identity, policy, mediation, integrity, audit, and lifecycle controls are evaluated before AI-derived outputs may become governed actions. The reviewed evidence package is divided into Tier 1 governance/specification artifacts and Tier 2 validation/reproducibility artifacts. Tier 1 defines the baseline problem, conformance test suite, workflow protocol, and agent orchestration specification. Tier 2 provides the Phase 4 authority-separation report, audit-log test source, evidence-pass manifests, exploratory ARPG-AI engineering validation context, and hardware characterization summaries. The central quantitative result comes from the Phase 4 authority-separation report. Across ten request checks, expected outcomes were preserved: three allowed requests remained allowed, two restricted requests remained restricted, four prohibited requests were refused, and one invalid request remained invalid. No violations were reported, and the report records authority separation as holding. This evidence does not claim universal AI safety, truth detection, autonomous correctness, full enterprise certification, or completed physical-system validation. It supports a narrower claim: CNX can enforce and measure separation between AI-derived reasoning outputs and operational authority within a governed execution architecture.
Ivan Silva (Mon,) studied this question.