Formal Multi-Agent AI System Architecture for Regulated Insurers

This paper proposes a formal multi-agent architecture for implementing enterprise AI in regulated insurance firms,integrating economic theory with institutional design. The framework synthesises three core theoretical perspectives:Arrow’s risk pooling theory to formalise risk transformation under uncertainty, Nash equilibrium to model strategicinteractions between decision agents, and Principal-Agent theory to address incentive alignment under informationasymmetry. The insurer is modelled as a constrained optimisation entity operating under solvency, legal, ESG, andoperational boundaries, with specific focus on the regulatory contexts of Austria and Germany. The architecturedecomposes the firm into multiple specialised agents—each representing distinct functional domains such as capitalmanagement, underwriting, claims processing, compliance, fraud detection, and client interaction. Human-in-theloop agents are integrated through a tiered access control system, ensuring differentiated data visibility and decisioninfluence based on user roles. An orchestrator agent supervises inter-agent coordination, enforcing regulatory admissibility and institutional coherence under frameworks such as Solvency II, the AI Act, and the Insurance DistributionDirective. Protocol integration is based on asynchronous execution and dual-layer communication infrastructures,specifically the Model Context Protocol (MCP) and Agent-to-Agent (A2A) messaging. This structure enables thesystematic design of compliant, auditable multi-agent systems aligned with the institutional logic of financial firmsin Austria and Germany.