As artificial intelligence develops rapidly, we are standing at a critical crossroads. With the rapid development of large language models and intelligent agent technology, how to enable these intelligent agents to collaborate efficiently and how to enable them to seamlessly connect with external data sources and tools has become an important bottleneck restricting the large-scale implementation of AI applications. Google's recently launched Agent2Agent Protocol (A2A) and Anthropic's previously released Model Context Protocol (MCP) were born to solve these challenges. Together, they have built a new paradigm for intelligent agent interaction and data connection, providing more possibilities for the future of AI engineering.

A2A: Breaking down the walls between agents

A2A's Essence and Mission

A2A (Agent2Agent Protocol) is an open protocol launched by Google at the Google Cloud Next conference in April 2025, which aims to provide a standardized way for AI agents to interact with each other. Simply put, A2A allows different AI agents to "know" and "talk" to each other, no matter which company they are developed by or what platform they run on.

Before the emergence of A2A, various intelligent agents were like city-states with high walls, and it was difficult for them to communicate and collaborate with each other. When users need to complete a complex task involving multiple systems, they often need to manually switch between multiple applications and copy information from one place to another, which is inefficient and prone to errors. The emergence of A2A is precisely to break these "system islands" and enable various intelligent agents to work together and divide the work and cooperate like human teams.

The core issues that A2A solves

The A2A protocol mainly solves the following key issues:

First, A2A enables interoperability between agents. Before this, agents developed by different vendors and different frameworks could not communicate directly, and users had to switch back and forth between different systems. A2A is built on existing popular standards such as HTTP, SSE, and JSON-RPC, and provides a common interaction language that enables any agent that follows the protocol to communicate with each other.

Secondly, A2A supports collaboration on long-term tasks. Many tasks in the real world are not completed instantly and may take hours or even days. A2A allows agents to maintain long-term conversations, exchanging status updates and intermediate results until the task is completed. This capability is particularly important for complex enterprise workflows.

Third, A2A focuses on multimodal interaction. The world of AI is not limited to text. A2A supports data exchange in multiple modes such as audio, image, and video, making the collaboration between intelligent agents richer and more natural.

How A2A works

The core of the A2A protocol is to facilitate communication between a "client agent" and a "remote agent". The client agent is responsible for conceiving and communicating tasks, while the remote agent is responsible for executing those tasks. This interaction includes four key capabilities:

Capability Discovery: Agents expose their capabilities through "Agent Cards" (JSON format), allowing other agents to find the best partner to perform a specific task.

Task management: A2A defines a complete task life cycle, supporting everything from simple immediate tasks to complex long-term tasks. Agents can continuously synchronize their states to ensure that each other is aware of the latest progress of the task.

Collaborative communication: Agents can exchange contextual information, responses, artifacts, or user instructions, enabling true multi-agent collaboration.

User experience negotiation: Agents can negotiate the display format of content to adapt to different user interface capabilities, such as iframes, videos, or web forms.

MCP: A bridge between the model and the outside world

The origin and significance of MCP

MCP (Model Context Protocol) is an open standard protocol launched by Anthropic in November 2024, which aims to unify the communication between large language models (LLMs) and external data sources and tools. If A2A enables intelligent agents to talk to each other, then MCP enables intelligent agents to "see" and "touch" the data and tools of the outside world.

Before the emergence of MCP, using external data in AI models often required copying and pasting or uploading and downloading, which was very cumbersome. Even the most powerful models were limited by data isolation, forming information islands. MCP directly builds a bridge between AI and data, allowing models to directly access and operate local and remote data.

Core functions of MCP

The MCP protocol uses a client-server architecture with several core concepts: the MCP host (LLM application that initiates requests), the MCP client (maintains a connection with the MCP server), the MCP server (provides context, tool, and prompt information), and local and remote resources.

MCP servers can provide three main types of functions: resources (data that can be read by clients), tools (functions that can be called by LLMs), and prompts (pre-written templates). These functions enable MCP to provide rich contextual information and operational capabilities for AI applications.

The workflow of MCP is as follows: the MCP client first obtains the list of available tools from the server and sends the user query and tool description to the LLM. The LLM decides whether to use the tool and which tools to use. If the tool needs to be used, the client will execute the corresponding call through the server and return the result to the LLM, which finally generates a response to the user.

MCP security mechanism

MCP greatly reduces the number of links that directly contact sensitive data and reduces the risk of data leakage through standardized data access interfaces. At the same time, MCP has built-in security mechanisms to ensure that only verified requests can access specific resources, adding a line of defense for data security.

For example, the MCP server controls its own resources and does not need to provide sensitive information such as API keys to the LLM provider. In this way, even if the LLM provider is attacked, the attacker cannot obtain this sensitive information.

A2A and MCP: Complementarity rather than competition

The essential difference between the two protocols

Although both A2A and MCP are open protocols designed to enhance AI capabilities, they focus on different core issues. As mentioned earlier, MCP is a standard for connecting LLM with data, resources, and tools, or in other words, it is becoming a standardized "function call" across different models and frameworks, greatly reducing the complexity of connecting agents with tools and data.

A2A focuses on another issue: how agents collaborate with each other. A2A is an application layer protocol that enables agents to collaborate in a natural way. It allows agents to communicate as "agents" rather than as tools.

To illustrate the difference with a concrete metaphor: imagine a car repair shop that employs mechanics who use specialized tools (such as jacks, multimeters, and socket wrenches) to diagnose and fix problems. MCP is like a protocol that connects these mechanics (agents) to their structured tools (e.g. "Raise the platform 2 meters", "Turn the wrench 4 mm to the right"). A2A, on the other hand, is a protocol that enables end users or other agents to collaborate with the repair shop staff (e.g. "My car makes a rattling sound"), supporting continuous two-way communication and evolving plans to achieve goals.

Possibility of collaboration

The relationship between A2A and MCP is not mutually exclusive but complementary. Google has also made it clear that it hopes A2A can be widely adopted as a supplement to MCP, thereby promoting the development of the intelligent ecosystem.

In practical applications, MCP provides agents with the ability to interact with the outside world, while A2A enables these agents with different capabilities to work together. For example, an agent with MCP capabilities can access the company's CRM system to obtain customer data, while another agent may be good at analyzing financial data. Through the A2A protocol, these two agents can collaborate and jointly provide the company with a more comprehensive customer value analysis.

The value brought by this kind of collaboration is far greater than the simple addition of a single agent. Just like the division of labor and cooperation in human society, experts in different fields can solve complex problems through effective communication. AI agents will also achieve unprecedented collaboration capabilities through the combination of A2A and MCP.

The impact of A2A and MCP on AI application engineering

Changes in general tool scenarios

In the general tool scenario, the combination of A2A and MCP will completely change the way we interact with software. Traditionally, users need to learn how to operate different software and switch between multiple applications to complete complex tasks. With the support of A2A and MCP, users only need to describe the tasks they want to complete, and the intelligent agent will automatically call the appropriate tools and data sources to work together to complete the task.

For example, a simple request to "prepare slides for my presentation" may trigger the collaboration of multiple agents: one responsible for understanding user needs and planning content, one responsible for collecting relevant information from the Internet and corporate knowledge base, and another dedicated to designing slides responsible for the final layout and beautification. These agents collaborate seamlessly through the A2A protocol, and each agent accesses the required data and tools through MCP.

Transformation of internal enterprise scenarios

For internal enterprise applications, the value of A2A and MCP is even more significant. Enterprises usually have multiple isolated systems - from CRM to ERP, from HR to financial systems, and information is scattered in different databases and applications. Traditionally, integrating these systems requires complex API development and data conversion.

But with A2A and MCP, enterprises can equip each system with dedicated agents, which access the data and functions of their respective systems through MCP and collaborate with each other through A2A. For example, the agent in the sales department can collaborate with the agent in the finance department to jointly analyze customer value and profitability; the HR agent can collaborate with the project management agent to optimize staff allocation and skill training.

This approach not only lowers the technical threshold for system integration, but also creates new possibilities for collaboration, maximizing the value of data and functions.

A new paradigm for cross-enterprise collaboration

The potential of A2A is not limited to the internal operations of enterprises, but may also completely change the way enterprises collaborate with each other. Imagine a supply chain scenario: manufacturers, logistics companies, and retailers each have their own intelligent systems. Through the A2A protocol, the intelligent systems of these enterprises can collaborate with each other under the premise of authorization, realizing full automation from production planning, logistics scheduling to inventory management.

In this cross-enterprise collaboration, security and data privacy are critical. The A2A protocol supports enterprise-level authentication and authorization mechanisms to ensure that data exchange during the collaboration process is strictly controlled. Enterprises can precisely define which data and functions can be accessed by external agents, thereby striking a balance between open collaboration and privacy protection.

The technical ideas for this cross-enterprise intelligent agent collaboration may include: border intelligent agents as a bridge for interaction within and outside the enterprise, a unified identity authentication and authorization system, and a security mechanism based on a zero-trust architecture.

The future of standards management: Towards a neutral foundation

From company-led to community-led

Currently, A2A is promoted by Google and MCP is led by Anthropic, but as the influence of these protocols grows, it may become a trend to hand them over to a neutral foundation for management. This development trajectory is not uncommon in the field of technical standards. For example, around 2015, the widespread adoption of container technologies such as Docker brought new challenges. Different companies and organizations developed their own container runtimes and orchestration tools, resulting in a fragmented ecosystem, lack of interoperability and standardization. The emergence of tools such as rkt and containerd has increased complexity, and the success of Kubernetes has highlighted the need for a neutral organization to coordinate these efforts and promote standardization.

There are several clear advantages to having A2A and MCP managed by a neutral foundation: avoiding conflicts of interest that may result from control by a single company, encouraging broader community participation and contributions, and ensuring the long-term stability of the standards.

Possible paths and challenges

The path for A2A and MCP to move toward neutral foundation management may include: first expanding the scope of participating companies, gradually establishing an open governance structure, and finally transferring intellectual property and decision-making power to the foundation. Google recently announced that more than 50 technology companies have participated in the development of the A2A protocol, including Atlassian, Box, Cohere, Intuit, Langchain, MongoDB, PayPal, etc. This is an important step towards a neutral governance model.

However, this process also faces challenges. Different companies may have different business interests and technical routes, and coordinating these differences requires time and compromise. In addition, how to ensure the independence and professionalism of the foundation and avoid being controlled by specific forces is also an issue that requires careful design.

The future of AI engineering standards

As AI technology matures, we may see more engineering standards like A2A and MCP emerge, covering all aspects of AI development, deployment, monitoring, and governance. These standards may eventually converge under one or several neutral foundations focused on AI engineering, similar to CNCF's position in the cloud native field.

In this future vision, developers can build AI applications based on standardized interfaces and protocols without having to worry about differences and lock-in in the underlying technologies. Enterprises can more easily combine AI components from different suppliers to build solutions that meet their needs. Ultimately, the entire AI ecosystem becomes more open, innovative, and prosperous because of this standardization.

Conclusion

The emergence of A2A and MCP marks a new stage in the development of AI - the evolution from single intelligence to collaborative intelligence. Just as the Internet Protocol (TCP/IP) laid the foundation for global information exchange, A2A and MCP may also become the infrastructure of the intelligent network, allowing various AI systems to solve complex problems through effective communication and collaboration like human society.

For AI application developers, understanding and mastering these protocols will become an essential skill; for enterprises, adopting these standards will help build more flexible and valuable AI solutions; for the entire industry, the development of open standards will promote innovation and competition.

In this new era of intelligent collaboration, we may rethink the nature of software, the way people interact with machines, and the design of organizational structures and workflows. A2A and MCP are not only technical protocols, but also the cornerstones of building the future intelligent world. "Individual intelligence may be limited, but through effective collaboration, collective intelligence can solve the most complex challenges facing humanity." A2A and MCP may be the key to unlocking this collective intelligence.