Provable Autonomy: The Governance Architecture for Mission-Critical AI

As AI systems assume autonomous decision-making authority in mission-critical environments — from healthcare triage to financial trading to infrastructure management — the need for provable governance becomes existential. Traditional governance approaches based on policy documents and periodic audits are fundamentally inadequate for systems that make thousands of consequential decisions per second without human intervention. This paper introduces a governance architecture based on provable autonomy: the ability to formally demonstrate that an AI system's autonomous actions remain within defined governance boundaries under all operating conditions.

The architecture combines formal verification methods adapted from safety-critical systems engineering, runtime monitoring with mathematical guarantees, and governance-as-code approaches that embed compliance constraints directly into AI system architecture.