Google's Agent2Agent (A2A) framework details

MCP, there are already many practical interpretations on the market, but as A2A, which is often compared, there is very little content. Today, I will explain the architecture of A2A in detail.

Imagine you walk into a busy office, with talented experts dealing with various complex projects. In one corner, research analysts are mining data; next to it, design experts are drawing visual solutions; on the other side, logistics coordinators are organizing freight. When these experts need to collaborate, they just need to talk to each other—sharing information, asking questions, integrating talents, and solving tasks that cannot be accomplished by individuals alone.

But if every expert is enclosed in a soundproofing room, and can still do their job well, but cannot communicate with others, the collective effectiveness of the entire office will collapse instantly.

This is exactly the challenge facing AI agents at present. Although individual AI systems are becoming stronger on certain specialized tasks, they often fail to collaborate efficiently. This is where the Agent-to-Agent (A2A) framework comes in – a communication mechanism that allows AI systems to work together like teams.

Why AI agents need "dialogue"

Today's AI world is like a "specialized island": some agents are good at scheduling, some are proficient in data analysis, and some are good at writing creativity. But even if these expertise are combined to solve more complex problems, they are usually fighting each other.

Imagine a seemingly simple request: "Help me plan my business trip to Chicago next month."
This task actually requires multiple expertise:

• Schedule management: Find available time

• Travel Knowledge: Book the right flights and hotels

• Budget Control: Make a cost-effective choice

• Geographical intelligence: efficiently arrange meeting locations and times

We can, of course, build a super system that coordinates all these functions—an omnipotent "super agent". But there are two major problems with doing this:

1. Development complex: Every new feature added must be integrated into the core system, which may destroy existing features.

2. Repeat the wheel : There are already mature flight booking, calendar management, and other services on the market, and there is no need for every company to rebuild it.

A2A's solution is: let you focus on building the agent you are best at, and other functions are achieved by connecting other expert agents. This modular approach allows multiple specialist agile teams to collaborate on solving more complex problems without having to do it from scratch each time.

If there is a lack of a communication mechanism, each specialized system can only handle a small part of the problem, and users have to manually coordinate between them and become a "human dispatching center".

Build a common language for AI

A2A provides a common language that allows AI agents developed by different backgrounds and by different teams to communicate with each other. Just like the unified use of English or Mandarin in international companies, collaboration becomes possible once a common language is established.

A2A not only defines how information is exchanged, but also how tasks are coordinated in a time dimension:

• Introduction to the protocol: Agents can discover and understand each other's abilities through the "Agent Card" (similar to digital resumes).

• Task Management: The agent can assign tasks and track progress. For example, a calendar agent can formally make a request to the travel agent and monitor the task status.

• rich communication formats: Not only limited to text, but also supports collaborative formats such as pictures, structured data, files, etc.

• Clarification mechanism: If the task information is unclear, the agent can pause the task and request clarification, just as a human colleague would ask additional questions.

A2A How to Work: Behind the Scenes Collaboration

Suppose you say to your AI assistant, “Help me plan my daughter’s birthday party next weekend.”

Behind the scenes, you can't see, the main assistant (such as Alex) will recognize that the request involves multiple areas and utilize A2A Collaboration like this:

1. Discovering expert agents: Alex will be on their Agent Card. Find the agents who are good at event planning, catering suggestions, and invitation design in the catalog. Technically, it will request the Agent Card of these agents (usually hosted in JSON files like https://agent-domain/.well-known/agent.json ) to list capabilities, communication endpoints, and authentication information.

2. Create a task and send a request:

• Send a request to the event planning agent: "Recommended venues and event plans for an 8-year-old girl's birthday party (next Saturday afternoon). ”

• Send a request to the event planning agent: "Provide cake and food plans for 12 children and 6 adults."

• Task status management

• Task status management:

• Each task has a unique ID, and the status is from "Committed → Processing → Completed/Failed/Pending Supplementary Information".

• If the activity planning agent responds: "What is your budget?" (the status is changed to input-required), Alex will update the task status based on the user profile or ask the user.

• The catering consultant returns structured menus, prices and dietary preference data (DataPart), which Alex can convert to a user interface display.

• Design agents to create invitation letter image files and encapsulate them as FilePart into Artifacts, marking the completion of the task.

• Real-time progress flow: Long-term tasks can update progress in real time through SSE (Server-Sent Events).

• Authentication mechanism: Agencies are used to authenticate using enterprise-level agreements such as OAuth and API Key.

In the end, you only talk to Alex, and you don’t have to worry about how many AIs are scheduled behind it. The whole process is coordinated and smooth, and the results are naturally consistent. This is the charm of A2A.

A2A's positioning in the AI ​​ecosystem

A2A does not exist in isolation, it is part of the broader trend in AI interoperability. Another important protocol is MCP (Model Context Protocol), which focuses on making a single agent better use of tools and context.

It can be understood in this way:

• A2A : It is a collaborative agreement that allows smooth communication between colleagues.

• MCP : Like equipping each employee with the tools and information they need.

They do not conflict, but complement each other. An agent can obtain the tools and context for performing tasks through MCP and then collaborate with other agents through A2A to complete multi-step tasks.

This represents a transformation in AI design concept - from the "universal island" model to "specialized network collaboration", just as human society evolved into the division of labor and collaboration.

A2A's technical architecture

Behind these popular metaphors, A2A actually implements a complete set of technical mechanisms to make the collaboration of agents possible:

1. Client-server model: In any A2A interaction, one agent is the client (initiator) and the other is the server (responder). Roles can be switched according to context.

2. Agent Card (ability card): A list of capabilities in JSON format, usually hosted on standard paths (e.g. /.well-known/agent.json ), including:

• Agency capability and support operation

• Communication endpoint URL

• Certification method requirements

• Supported message and content types

• Task Lifecycle:

• Each task has a unique ID

• Life cycle includes: submitted, working, input-required, completed, failed, cancelled

• It also contains meta information such as timestamps and task attribution

• Message Structure:

• TextPart : Normal text or rich text content

• DataPart : Structured data (such as JSON)

• FilePart : Binary or file reference

• Each message consists of several parts:

• Each part has a MIME type description of its content format

• Transport Protocol :

• Usually used HTTP/HTTPS

• Standard REST interface is used for task creation and update

• Server-Sent Events (SSE) are used for real-time task progress push

• Optional Webhook supports asynchronous notification

• Security mechanism :

• OAuth 2.0 Process

• API Key

• JWT Token

• Access control permissions

This architecture is enough to support complex collaboration processes from simple Q&A to long-running ones.

Summary: Collective wisdom is greater than the sum of individual abilities

The true power of A2A is that we are no longer limited to the functions of a single AI, but build a "specialized collaboration network." Just as human society has made rapid progress due to division of labor and collaboration, AI will also achieve a qualitative leap on the basis of collaboration between agents.

A2A brings the following advantages:

• Modular evolution: Just replace a certain agent without refactoring the entire system

• Progressive automation: Tasks that originally required manual coordination can be automatically completed by the agent

• Release of specialization : No longer pursues "all-around but mediocre" AI, but creates the strongest agent in various fields

The future AI is not an omnipotent superintelligence, but a group of efficient and collaborative professional agents. A2A is the communication infrastructure that enables this collaborative effort to help AI truly enter a new era of dealing with complex real-world tasks.

Looking forward

As frameworks such as A2A promote the continuous improvement of the collaboration capabilities of AI agents, we will move from "control tools" to "delegated tasks". The user experience will become easier and simpler, while the capabilities of AI will become stronger and stronger - this is a sign of mature technology.

Just like a group of colleagues sitting around the conference table brainstorming, AI agents can integrate their respective professional perspectives through the A2A protocol and propose solutions that cannot be achieved by any single agent.

This is exactly what A2A promises - not just smarter individual AI, but smarter collaboration.