Ethical Hyper-Velocity (EHV): A Hardware-Rooted Zero-Trust Runtime Enforcement Architecture for Agentic AI Systems

Abstract

As autonomous agentic systems scale across regulated critical infrastructures, the lack of mechanistic, hardware-rooted enforcement for high-frequency policy updates presents a fundamental safety gap. We present Ethical Hyper-Velocity (EHV), a governance-aware runtime enforcement architecture for agentic systems that combines Grammar-Constrained Decoding (GCD) for inline policy-constrained token generation, Causal Graph CRDT-based policy synchronization with vector-clock ordering, hardware-attested execution in Trusted Execution Environments (TEEs), and OSCAL-formatted machine-readable audit logging. Unlike retrospective auditing frameworks (ISO/IEC 42001, NIST AI RMF) that introduce 14-30 day policy latencies, EHV relocates the Policy Enforcement Point (PEP) into the inference pipeline via a Governance-Aware Just-In-Time (JIT) Compiler. Under explicitly stated assumptions, the architecture reduces enforcement latency, improves traceability, and supports formal verification of safety invariants in a bounded model. We demonstrate via TLA+ model checking that non-compliant agentic actions were unreachable in the verified bounded operating state space (1,738 states generated, 324 distinct, depth 8, zero violations). Under these conditions, O(1) runtime enforcement reduces the traditional trade-off between deployment velocity and governance integrity, targeting Governance Latency from O(days) toward O(1). EHV's differentiating contribution is the integration of GCD, Causal CRDT, TEE attestation caching, and bounded formal verification into a single, hardware-rooted enforcement architecture -- a combination not achieved by any contemporaneous system. The architecture is demonstrated through a pediatric oncology dosage use case, with applicability to regulated critical infrastructures including healthcare, financial compliance, and critical infrastructure control.