Universal Authority Separation with Cryptographic Decision Integrity: Performance Evaluation of a Deterministic External Governance Layer for Agentic AI

Agentic AI systems that autonomously generate and execute proposals require reliable mechanisms to enforce safety boundaries. Existing approaches rely primarily on probabilistic internal alignment techniques, which introduce computational overhead, latency, and limited verifiability. This report evaluates UAS (Universal Authority Substrate) + Aletheia, an external governance layer that applies deterministic constraints to proposed actions and attaches a cryptographic integrity check to every decision for functional verification. UAS performs rapid gating; Aletheia serializes and seals the outcome. Stress testing on consumer hardware (Intel N95, 4 cores, 16 GB RAM) shows sustained throughput of 3,900-4,400 requests per second over runs of 100,000+ proposals, with 100% functional seal validity across 300,000 evaluations, zero enforcement errors, and average latency of 800-850 μs (P95 ≈ 2.5 ms). Ablation experiments indicate the integrity check adds negligible throughput cost. Scaling projections for industrial GPU clusters are provided, along with energy implications. Limitations, including constraint expressiveness and the need for adversarial testing, are discussed. The results suggest external deterministic governance may serve as a viable complement to probabilistic alignment methods.