A Survey of AI Agent Protocols (2504.16736v3)

Abstract: The rapid development of LLMs has led to the widespread deployment of LLM agents across diverse industries, including customer service, content generation, data analysis, and even healthcare. However, as more LLM agents are deployed, a major issue has emerged: there is no standard way for these agents to communicate with external tools or data sources. This lack of standardized protocols makes it difficult for agents to work together or scale effectively, and it limits their ability to tackle complex, real-world tasks. A unified communication protocol for LLM agents could change this. It would allow agents and tools to interact more smoothly, encourage collaboration, and triggering the formation of collective intelligence. In this paper, we provide the first comprehensive analysis of existing agent protocols, proposing a systematic two-dimensional classification that differentiates context-oriented versus inter-agent protocols and general-purpose versus domain-specific protocols. Additionally, we conduct a comparative performance analysis of these protocols across key dimensions such as security, scalability, and latency. Finally, we explore the future landscape of agent protocols by identifying critical research directions and characteristics necessary for next-generation protocols. These characteristics include adaptability, privacy preservation, and group-based interaction, as well as trends toward layered architectures and collective intelligence infrastructures. We expect this work to serve as a practical reference for both researchers and engineers seeking to design, evaluate, or integrate robust communication infrastructures for intelligent agents.

A Systematic Overview of AI Agent Protocols

The paper "A Survey of AI Agent Protocols" by Yingxuan Yang et al. provides a comprehensive analysis of existing communication protocols tailored for LLMs. The rapid proliferation of LLM-based agents across various domains like customer service, healthcare, and data analysis highlights the urgency for standardized communication protocols. Such structures could significantly enhance collaboration, interoperability, and scalability in intelligent agent systems, thereby supporting complex real-world applications.

Classification and Comparative Analysis

The authors propose a two-dimensional classification framework to systematically organize agent protocols. Protocols are categorized by object orientation into context-oriented and inter-agent types, and by application scenario into general-purpose and domain-specific categories. This approach offers clarity in navigating the extensive landscape of agent protocols, which are currently fragmented due to proprietary standards and varied interfaces.

• Context-Oriented Protocols: MCP, as a prominent example, facilitates standardized tool connection, enhancing interoperability and scalability while ensuring privacy by decoupling tool invocations and LLM responses.
• Inter-Agent Protocols: These protocols, such as ANP and A2A, support communication between multiple agents, addressing interoperability but necessitating robust network structures to sustain collaboration.

The authors conduct a comparative analysis across dimensions including scalability, security, and reliability, shedding light on the strengths and weaknesses of various protocols. They also discuss challenges, like the absence of benchmarks, lack of adaptability, and security risks, which need addressing to design effective protocols for LLM agents.

Implications and Future Directions

The establishment of a unified protocol has implications for both theoretical developments and practical applications. Theoretically, it would facilitate research towards understanding agent communication dynamics, collective intelligence formation, and evolving cogent architectures. Practically, it would streamline system integration, reduce development complexity, and support novel business models.

Looking ahead, speculative developments focus on:

• Evolvable Protocols: Future systems may treat protocols as learnable components, supporting adaptability through dynamic negotiation and autonomous formatting.
• Privacy-preserving Mechanisms: Research should explore authorization and federated learning-based collaboration methods to protect sensitive data during agent interactions.
• Layered Architectures: Separating concerns in layered architectures will improve modularity and scalability, aligning agent behavior with societal values.

These advancements are predicted to redefine intelligent agent interaction, introducing concepts of collective intelligence and dedicated Agent Data Networks optimized for agent communication.

Conclusion

This paper undoubtedly acts as a practical guide for researchers and engineers committed to designing, evaluating, and integrating robust communication infrastructures for intelligent agents. By outlining existing protocols and identifying areas for improvement, the authors foster insightful discussions on the future of agent protocols that could eventually lead us towards a cohesive, intelligent agent ecosystem. As AI systems become increasingly prevalent and complex, addressing the challenges and opportunities of standardized communication protocols is crucial for their evolution and effectiveness in real-world applications.