AI Agent "social network" is here! The latest research reveals a panoramic view of AI Agent communication protocols

Just like the Internet requires TCP/IP and HTTP protocols, collaboration between AI Agents also requires standardized communication protocols. This article will give you an in-depth understanding of the current status and future of AI Agent protocols, and see how the academic community builds a "social network" between Agents.

1. AI Agent Communication Protocol: A Key Step to Breaking Information Silos

Have you ever thought about how AI assistants like ChatGPT and Claude should "talk" to each other when they need to communicate and collaborate with each other?

With the rapid development of large language models (LLMs), various AI agents have been widely used in customer service, content creation, data analysis and even medical assistance. However, when these agents need to interact with external tools or collaborate with each other, a serious problem emerges: the lack of standardized communication protocols .

Just as the early Internet was divided by various incompatible systems and limited connections, the current AI Agent ecosystem is also facing a similar "islandization" dilemma. Looking back at history, TCP/IP and HTTP protocols not only solved technical problems, but also opened up an unprecedented era of global connectivity, innovation and value creation, completely changing human society.

Likewise, a unified agent communication protocol will not only solve current interoperability issues, but will also hopefully create a revolutionary "smart network" where different forms of intelligence can flow freely between systems, generating a collective intelligence that is more powerful than any single component.

2. Two major categories of agent protocols: context acquisition and agent-to-agent interaction

For the first time, researchers proposed a systematic two-dimensional classification framework to organize various agent protocols:

First dimension: object-oriented type

(1) Context-oriented protocol : helps agents obtain external data, call tools and services

(2) Inter-agent protocol : promoting communication and collaboration between different agents

The second dimension: application scenarios

(1) General protocol : applicable to a wide range of scenarios and various types of agents

(2) Domain-specific protocols : optimized for specific domains or usage scenarios

3. Context-Oriented Protocol: Agent’s “External Brain”

Although LLMs have powerful language understanding and reasoning capabilities, they cannot rely solely on internal knowledge to respond to complex queries. Therefore, agents usually need to autonomously decide when and which external tools to call and perform operations through these tools to obtain the necessary context.

In early development, the tool usage capabilities of agents are usually fine-tuned through formatted function call datasets. However, this approach faces multiple challenges due to the lack of standardized and unified context-oriented protocols.

The Model Context Protocol (MCP) is a groundbreaking general-purpose context-oriented protocol developed by Anthropic. It provides a standardized way for agents to connect to external data, tools, and services more simply and reliably. The core architecture of MCP consists of four components:

(1) Host : refers to the LLM Agent, which is responsible for interacting with users, understanding and reasoning about user queries, selecting tools, and initiating strategic context requests

(2) Client : connects to the Host, provides a description of available resources, and is responsible for initiating execution context requests

(3) Server : connects to the resource and provides the required context to the client

(4) Resource : refers to resources such as data, tools or services

MCP solves the fragmentation problem in the LLM ecosystem by introducing an open and standardized calling protocol that decouples tool usage from specific underlying LLM provider and context provider interfaces. In addition, MCP enhances privacy and security by decoupling tool calls from LLM responses, allowing sensitive information to remain local, thereby reducing the risk of data leakage.

4. Inter-Agent Protocol: Building an Agent "Social Network"

With the development of LLM and Agent technology, multi-agent collaboration has attracted more and more attention. In some large-scale, complex, and inherently decomposable or distributed tasks, multi-agent methods can improve efficiency, reduce costs, and provide better fault tolerance and flexibility.

The inter-agent protocols that have been proposed include:

(1) Agent Network Protocol (ANP) : Developed by the open source technology community, it aims to achieve interoperability of agents in various heterogeneous fields.

(2) Agent2Agent Protocol (A2A) : An agent collaboration protocol proposed by Google, which aims to achieve seamless agent collaboration.

(3) Agora : Leveraging LLM’s capabilities in natural language understanding, code generation, and autonomous negotiation, Agora enables agents to adopt different communication protocols based on context.

Although these protocols all focus on the interaction between agents, they differ in problem domains, application scenarios, and implementation strategies.

5. Evaluation dimensions of agent protocols: not just performance and functionality

In the rapidly evolving field of agent communication protocols, static performance or feature comparisons quickly become obsolete. For example, MCP initially lacked support for HTTP and authentication mechanisms when it was introduced in November 2024, but by early 2025, it had incorporated HTTP Server-Sent Events (SSE) and authentication, and had moved to HTTP Streaming.

The researchers identified seven key dimensions for evaluating agent protocols:

(1) Efficiency : managing throughput, minimizing latency, optimizing handshake overhead, etc.

(2) Scalability : The ability to maintain performance and availability as the number of nodes or connections grows exponentially

(3) Security : Protecting the system from malicious behavior and data leakage

(4) Reliability : ensuring that messages and tasks are delivered and processed correctly under various conditions

(5) Interoperability : The ability to work seamlessly with systems from different vendors and architectures

(6) Evolvability : The ability of the protocol to adapt to changes and new requirements

(7) Simplicity : ease of use and understandability of protocol design

6. Thinking

Looking ahead, the researchers foresee several key development directions for the Agent protocol:

(1) Evolvable protocols : Able to self-adjust and adapt to the ever-changing Agent ecosystem

(2) Privacy Protection Protocol : Protecting user privacy and data security in the data-sensitive era

(3) Group coordination protocol : Supporting effective collaboration and decision-making among multi-agent teams

(4) Layered architecture : decomposing the protocol into specialized layers, similar to the TCP/IP model

(5) Collective intelligence infrastructure : promoting knowledge sharing and dynamic collaboration among agents

Just like the basic protocols of the Internet, future agent communication standards are expected to usher in a new era of distributed collective intelligence and reshape how intelligence is shared, coordinated, and amplified among systems.

This study provides us with a panoramic view of AI Agent protocols, including a systematic classification, evaluation of key performance dimensions, and future development trends. As AI Agent technology develops, standardized communication protocols will become the key infrastructure connecting various intelligent agents, enabling them to dynamically form alliances, exchange knowledge, and jointly solve increasingly complex real-world problems .

Just as the TCP/IP and HTTP protocols ushered in the Internet era, the unified Agent protocol may usher in a new era of "smart Internet", in which AI is no longer an isolated individual, but a network capable of collaboration, sharing and collective evolution.