Governing Highly Capable Human–Machine Systems Through Authority, Memory, and Consequence

Increasingly capable AI systems can generate proposals, invoke tools, and create institutional commitments. Their cognitive performance does not establish their authority to act. This conceptual systems article develops governed systemic intelligence as an assurance framework for human–machine systems, rather than redefining intelligence to include governance. It distinguishes capability, epistemic justification, operational authorization, and normative legitimacy. A structured synthesis of intelligence, augmentation, cybernetics, computer security, provenance, and AI oversight motivates eight architectural requirements: ratified purpose, capability–authority separation, bounded authorization, authoritative memory, consequence closure, ratified learning, governed continuity, and external verifiability. A unified traceability matrix connects each requirement to an evaluation criterion, evidence artifact, and omission counterexample. Predicates over an explicit system model separate governance conformance from measured augmentation and from the stronger claim of domain-superhuman performance. A worked hypothetical prior-authorization episode illustrates all eight requirements, while a numerical coherence-debt example makes unresolved discrepancies and missing outcomes visible. Mindful Machines and a conventional policy-and-workflow stack provide alternative implementation mappings. Previously reported implementations support component feasibility and governance-path execution but do not establish complete conformance, scalable oversight, or superhuman capability. The contribution is an assurance-case pattern that composes authority, memory, continuity, and correction obligations into testable claims, with explicit limits under adversarial behavior, institutional capture, and incomplete consequence observation.