提出MCP协议，让多智能体系统更高效协作。
Advancing Multi-Agent Systems Through Model Context Protocol: Architecture, Implementation, and Applications
- 设计统一的上下文共享与协调机制
- 在企业知识管理等场景提升性能表现
- 适合研究多智能体协同与系统架构者
多智能体系统是人工智能的重要进展，通过专业化智能体协作解决复杂问题。然而，这类系统面临上下文管理、协作效率和可扩展性等根本挑战。本文提出模型上下文协议（Model Context Protocol, MCP）框架，通过标准化上下文共享与协调机制，解决上述问题。我们建立了统一的理论基础，发展先进的上下文管理技术与可扩展的协调模式。在企业知识管理、协作研究和分布式求解等领域的实现案例中，相较传统方法显著提升性能。评估方法包含为多智能体系统定制的基准任务与数据集。本文识别当前局限、揭示新兴研究机会，并展望跨行业变革性应用。该工作推动更强大、协作性强、上下文感知的人工智能系统演进，以应对复杂现实挑战。
原文摘要 · Abstract (English)
Multi-agent systems represent a significant advancement in artificial intelligence, enabling complex problem-solving through coordinated specialized agents. However, these systems face fundamental challenges in context management, coordination efficiency, and scalable operation. This paper introduces a comprehensive framework for advancing multi-agent systems through Model Context Protocol (MCP), addressing these challenges through standardized context sharing and coordination mechanisms. We extend previous work on AI agent architectures by developing a unified theoretical foundation, advanced context management techniques, and scalable coordination patterns. Through detailed implementation case studies across enterprise knowledge management, collaborative research, and distributed problem-solving domains, we demonstrate significant performance improvements compared to traditional approaches. Our evaluation methodology provides a systematic assessment framework with benchmark tasks and datasets specifically designed for multi-agent systems. We identify current limitations, emerging research opportunities, and potential transformative applications across industries. This work contributes to the evolution of more capable, collaborative, and context-aware artificial intelligence systems that can effectively address complex real-world challenges.
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