Biological systems have evolved four distinct classes of collective and individual intelligence: swarm coordination (colonies), neuroscience-grade cognition (brains), immune-system governance (adaptive defense), and hippocampal memory (experience-based learning). Each of these has inspired isolated computational mechanisms---ant colony optimization, neural networks, artificial immune systems, complementary learning systems---but no framework integrates all four into a unified architecture for autonomous large language model (LLM) agents. This paper presents the first such integration. We describe a four-pillar architecture comprising: (1)~a multi-pattern swarm coordination layer that integrates eight biological coordination mechanisms into a unified organism for inter-agent orchestration; (2)~a neuroscience-inspired cognitive core that implements active inference, somatic markers, and dual-process routing for individual agent decision-making; (3)~an immune-system governance middleware providing adaptive guardrails, incident resolution, consensus scoring, and trust-zone management; and (4)~a cognitive memory system based on complementary learning systems theory with multi-resolution access, affordance-based retrieval, and consolidation via simulated dreaming. We present the integration architecture that bridges these pillars---where the swarm layer coordinates what agents do while the cognitive core decides how each agent acts---connected through a behavioral signaling protoco