As AI systems transition from content generation to autonomous execution—writing to databases, initiating transactions, modifying infrastructure—the governance problem shifts from observing behavior to authorizing action. Monitoring-based architectures document what occurred; they do not determine whether a specific action was permitted before execution. This paper analyzes a recurring structural ambiguity across recent cross-jurisdiction regulatory consultations: the under-specification of the enforcement primitive at the decision boundary where AI intent becomes real-world effect. Drawing on comparative analysis of EU, U.S., and Asia-Pacific governance frameworks, it distinguishes observability from authorization and argues that monitoring alone cannot satisfy enforcement-grade requirements in high-impact agentic deployments. The paper defines the architectural properties of an authorization boundary—non-bypassable runtime gating, deterministic verdict semantics, fail-closed defaults, policy-state binding, portable evidence artifacts, and independent replay capability—and proposes a Minimum Authorization Boundary Contract (MABC) for evidence-grade governance. The argument is structural rather than empirical. It derives governance requirements from engineering constraints inherent to autonomous systems and from the regulatory trajectory toward decision-level proof. The paper positions authorization infrastructure as a necessary layer beneath existing monitoring and compliance stacks in regulated, high-consequence AI environments.
Meyman et al. (2026) studied this question.