Demonstrates the limitations of manual review in governance evidence for organizations using agentic AI, suggesting deeper layers are needed.
Organizations deploying agentic AI under Microsoft’s Agent Governance Toolkit (AGK) or similar execution-layer frameworks are exposed to a persistent misconception: that documented risk assessments, manually reviewed policies, and runtime rule enforcement together constitute sufficient governance evidence. This paper argues that they do not. Manual documentation produces records of intent. Execution-layer enforcement produces logs of action. Neither produces governance invariants—the deterministic, lineage-anchored, continuously verifiable evidence that regulatory frameworks including the Colorado AI Act, the EU AI Act, and NIST AI RMF actually require at the moment of enforcement scrutiny. APR-Series is the substrate-layer architecture that produces those invariants. This paper explains the distinction, its regulatory consequences, and why the two layers are not interchangeable.
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Narnaiezzsshaa Truong (2026) studied this question.
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