Aegis Cortex: A Bio-Inspired, Zero-Trust Architecture for Homeostatic AI Agent Governance

Abstract: Current AI governance frameworks predominantly treat safety as an external perimeter, relying on prompt guardrails and post-hoc filters. While functional for static models, this paradigm fails when applied to Autonomous Agents capable of continuous reasoning and dynamic task execution. In such systems, external governance consistently lags behind internal logic drift and resource exhaustion. The central challenge of autonomous AI is not merely capability control; it is the absence of systemic homeostasis. This paper introduces Aegis Cortex, a structural architecture that shifts AI governance from external regulation to endogenous physiology. Rather than attempting to replicate human cognition, Aegis Cortex maps the homeostatic mechanisms of biological nervous systems to AI agent architecture, providing a framework for long-term stability under continuous internal conflict. The architecture introduces structural regulation through constitutional inheritance, module arbitration, and metabolic constraints, ensuring that intelligence is stabilized from within rather than policed from the outside. Three Synergistic Underlying Mechanisms: Global Runtime Inheritance: Ensures that during initialization and every state transition, the Agent forcibly inherits a "Global Security Kernel" that cannot be overwritten by business code. Establish a hard physical isolation between the safety baseline and local task optimization from the underlying State Bus. State Machine Routing & Deterministic Arbitration: Borrows from the circuit breaking and data plane isolation features in microservices architectures to introduce an independent Egress Conflict Arbitrator (ACC Gateway). Stripping away heavy cognitive or factual verification, it focuses purely on calculating strict compliance deviations and threat residuals in real-time with O(1) complexity. By enforcing static threshold arbitration, it physically usurps the control flow and flushes dirty data, preventing the LLM's internal alignment drift or prompt-induced hallucinations from ever crossing the enterprise network boundary. Compute Economics & Resource Constraints: References operating system-level resource quota management to introduce a Metabolic Scheduler. This redefines Token consumption as a dynamic variable controlled by an Instability Index. Through dynamic pricing and hard circuit-breaker thresholds, it ensures resource sovereignty remains independent of the Agent's generation logic, thereby supporting system-level high availability under open tasks. Significance: Aegis Cortex provides a theoretical and structural foundation for designing Autonomous Agents that remain stable under pressure. By defining computational resources and internal arbitration as core physiological components of the system, it establishes that the longevity of an intelligent agent depends on the rigorous regulation of its internal conflicts and metabolic boundaries.