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. Key Architectural Contributions: Neuro-Cognitive Mapping: Establishes a foundational correspondence between biological brain regions and AI agent functions, translating physiological homeostasis into actionable system architecture. Brain-Like Inheritance (Constitutional Kernel): Ensures that upon initialization, the Agent inherits an unalterable global constitutional kernel. This structurally establishes a long-term tension and balance between fundamental core principles and localized task optimizations. Inter-Regional Game & Arbitration: Drawing on the checks and balances inherent in the human brain's distributed decision-making, this architecture introduces a dedicated conflict arbitration layer. It is designed to resolve jurisdictional disputes among different functional modules, preventing the reasoning process from experiencing logic drift or cascading failures due to chaotic internal competition. Metabolic Constraint Logic: Inspired by the energy-regulating principles of the hypothalamus, this mechanism redefines computational power as a hard, system-level variable. Through precise quota management and interruption thresholds, resource constraints become an intrinsic part of the Agent's cognition, sustaining long-term homeostatic operation during open-ended 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.