Pre-Execution Admissibility Governance for Automated and Autonomous Systems

Modern automated and autonomous systems increasingly make decisions with irreversible, safety-critical, or system-wide consequences. While advances in artificial intelligence, optimization, and digital twins have improved decision quality, execution governance has lagged behind. Most deployed systems rely on post-execution monitoring, human-in-the-loop approval, confidence thresholds, or auditing mechanisms that intervene only after actions have already occurred. This paper proposes a fundamentally different approach: a pre-execution admissibility governance layer that governs execution authority before actions reach actuation or state-transition environments. Rather than attempting to correct or explain failures after the fact, the architecture structurally prevents inadmissible actions from executing at all. The proposed system evaluates proposed actions upstream of execution using admissibility criteria grounded in contextual state, evidentiary sufficiency, authority constraints, irreversibility, invariant system rules, and predictive validation. Authority is treated as a dynamically resolved, consumable resource that collapses deterministically under uncertainty rather than failing open. Invariant constraints define allowable system states independently of decision-generation logic and remain non-overrideable regardless of model confidence or optimization objectives. The paper details architectural components, operational modes, authority budgeting, irreversibility-aware enforcement, navigation-conditioned governance under degraded or denied signals, adversarial robustness considerations, and graceful degradation mechanisms. Digital twins are explicitly addressed and positioned as advisory tools that inform admissibility without conferring execution authority. By separating decision generation from execution authority, this work reframes system safety, compliance, and trustworthiness as a structural property of system architecture rather than an emergent behavior. The approach is applicable across cyber-physical systems, autonomous vehicles, robotics, infrastructure control, and digital execution environments, and aligns naturally with regulator expectations for prevention-first, fail-closed system design.