Aegis Cortex (AC-OS): An industrial-grade Zero-Trust AI Execution Runtime

Abstract: The deployment of generative Large Language Models (LLMs) in high-stakes industrial and commercial networks exposes a fundamental architectural friction: the irreconcilable conflict between the probabilistic nature of generative topologies and the strict deterministic requirements of critical environments. To resolve the inherent fragility of current Agent frameworks, this project open-sources Aegis Cortex (AC-OS)—an industrial-grade Zero-Trust AI Execution Runtime. AC-OS dismantles the traditional "evolutionary empowerment" paradigm by establishing a strict sovereignty contract: Cognitive Proposition (Noesis), Governance Arbitration (Aegis), and Physical Execution (Runtime). This architecture definitively strips the LLM of its direct peripheral invocation sovereignty. At its core, the system introduces a fully out-of-band biomimetic governance matrix: it leverages a Unified Anterior Cingulate Cortex (Unified ACC) protocol to evaluate cognitive logical entropy in real-time, and a Hypothalamus monitor to strictly enforce metabolic compute quotas by tracking the second derivative of token consumption. Driven by a natively engineered cross-temporal credit scoring engine that calculates the absolute Effective Risk (R_{effective}), AC-OS guarantees physical-level meltdowns and strict interceptions against slow data exfiltration, advanced persistent jailbreaks (APT-style probing), and logical hallucination deadlocks. By subordinating probabilistic cognition to deterministic governance, AC-OS ensures that autonomous agents remain strictly contained within industrial safety baselines. Reference Note: The specific risk control policies of the Aegis Ingress/Egress Gates in this system, as well as the underlying Token management architecture based on metabolic and quota concepts, are an evolution of our previous research. For detailed topological definitions and quota allocation logic, please refer to the previously published "Aegis: Cognitive Containment Boundary Specification for Enterprise AI Agents"