🤖 AI Summary
This work addresses the lack of systematic architectural approaches for enterprise-scale multi-agent collaborative systems, particularly in complex scenarios integrating human and AI agents. The authors propose a three-layer design pattern—comprising LLM agents, autonomous agents, and agent communities—that integrates principles from distributed coordination, formal modeling, and a role-protocol-governance structure. For the first time, this framework introduces formal collaboration protocols and role-based governance mechanisms into agent communities, enabling executable specification and verification of organizational, legal, and ethical rules. Validated through a clinical trial matching case study, the architecture demonstrates governable and verifiable human-AI collaboration, offering both formal verification capabilities and actionable design guidance for enterprise deployment.
📝 Abstract
The rapid evolution of Large Language Models (LLM) and subsequent Agentic AI technologies requires systematic architectural guidance for building sophisticated, production-grade systems. This paper presents an approach for architecting such systems using design patterns derived from enterprise distributed systems standards, formal methods, and industry practice. We classify these patterns into three tiers: LLM Agents (task-specific automation), Agentic AI (adaptive goal-seekers), and Agentic Communities (organizational frameworks where AI agents and human participants coordinate through formal roles, protocols, and governance structures). We focus on Agentic Communities - coordination frameworks encompassing LLM Agents, Agentic AI entities, and humans - most relevant for enterprise and industrial applications. Drawing on established coordination principles from distributed systems, we ground these patterns in a formal framework that specifies collaboration agreements where AI agents and humans fill roles within governed ecosystems. This approach provides both practical guidance and formal verification capabilities, enabling expression of organizational, legal, and ethical rules through accountability mechanisms that ensure operational and verifiable governance of inter-agent communication, negotiation, and intent modeling. We validate this framework through a clinical trial matching case study. Our goal is to provide actionable guidance to practitioners while maintaining the formal rigor essential for enterprise deployment in dynamic, multi-agent ecosystems.