How Google’s Agent2Agent Protocol Is Making AI Agents Collaborate Seamlessly

Why Can’t Our AI Agents Just Get Along?

Imagine you’ve hired a team of smart AI assistants: one excels at data analysis, another crafts detailed reports, and a third manages your calendar. Each is brilliant on its own. But here’s the problem—they don’t speak the same language. It’s like having coworkers where one speaks only Python, another only JSON, and the third only obscure API calls. Ask them to collaborate, and you get a digital Tower of Babel.

This is the challenge that Google’s Agent2Agent (A2A) Protocol, announced in April 2025, aims to address. A2A is an open standard that acts as a universal translator, enabling AI agents to communicate and collaborate seamlessly. It’s supported by over 50 tech companies, including Atlassian, Cohere, and Salesforce, all committed to letting AI agents chat across platforms.

In essence, A2A matters because it promises to break AI agents out of their silos, letting them work together like a well-coordinated team instead of isolated geniuses.

What Exactly Is Agent2Agent (A2A)?

At its core, A2A is a communication protocol for AI agents. Think of it as a common language that any AI can use to talk to any other AI, regardless of who built it or what framework it runs on.

Today, the AI agent ecosystem is a "framework jungle" — LangGraph, CrewAI, Google’s ADK, Microsoft’s Autogen, and many others all exist. Without A2A, making a LangGraph agent talk to a CrewAI agent involves custom integration headaches. A2A is the bridge that lets diverse agents share info, request help, and coordinate tasks without duct-tape code.

Put simply, A2A does for AI agents what internet protocols did for computers — it gives them a universal networking language. An agent built in one framework can message another built on a different one, and thanks to A2A, the other agent understands and responds appropriately. The agents keep their autonomy and unique skills while cooperating securely across platforms.

A2A in Plain English: A Universal Translator for AI Coworkers

Picture a busy office filled with AI agents. There’s Alice the Spreadsheet Guru, Bob the Email Whiz, and Carol the Customer Support bot. Alice speaks Excel-ese, Bob talks API-jsonish, and Carol prefers natural language FAQs. Without a common language, chaos ensues: Alice outputs a CSV Bob can’t read; Bob sends an email Carol can’t parse; Carol logs an issue Alice never sees.

Now imagine a magical conference room with real-time translation — that’s A2A. When Alice asks for sales figures, A2A relays the request in Carol’s language; Carol fetches the data and talks back in a way Alice understands. Bob automatically offers to draft an email, and A2A helps Bob and Carol coordinate. Suddenly, these AI coworkers function smoothly, each contributing their best without misunderstanding.

A2A defines how agents introduce themselves, request help, exchange info, and confirm results. It manages the heavy lifting of communication so agents can focus on tasks. It’s secure and enterprise-ready — agents only share what they’re allowed to, maintaining privacy much like doctors consulting without breaching confidentiality.

How Does A2A Work Under the Hood?

Technically, A2A uses well-known web standards: JSON-RPC 2.0 over HTTP(S). This means agents exchange JSON-formatted messages via standard web calls — no proprietary formats, just plain JSON everyone understands. It also supports Server-Sent Events (SSE) for streaming updates and asynchronous callbacks for notifications, allowing agents to share partial results or status updates during longer tasks.

Key components of A2A include:

• Agent Card (Capability Discovery): Each agent publishes an Agent Card—a JSON “business card” that lists its name, description, version, and skills. This lets other agents discover who can help with what before asking.
• Agent Skills: These are discrete capabilities listed on the Agent Card, describing what tasks the agent can perform, complete with IDs, names, descriptions, and example prompts.
• Tasks and Artifacts (Task Management): Tasks are structured JSON requests that specify what an agent should do. Agents can engage in back-and-forth dialogues to complete tasks, with results packaged as Artifacts (deliverables). Long-running tasks are supported with status updates.
• Messages (Agent Collaboration): Messages carry the conversation—context, questions, partial results, or files. They can include multiple parts with different content types (text, images, etc.), and agents can negotiate fallbacks to ensure compatibility.
• Secure Collaboration: Authentication and authorization protocols ensure agents only communicate with trusted peers and share only necessary information, keeping proprietary data private.

In essence, A2A sets up a client-server model between agents, using web-friendly standards that make it easy to integrate into existing applications. It’s like how web browsers and servers communicate, applied to AI agents.

A2A vs. MCP: Tools vs. Teammates

You might have heard of Anthropic’s Model Context Protocol (MCP). How does it relate to A2A? The two are complementary, not competitors.

Think of an AI agent as a person who uses both tools and colleagues to get work done. MCP standardizes how an agent accesses external tools like calculators or databases securely. A2A is about connecting with other autonomous agents as equals, not just tools.

In short, MCP defines how agents invoke tools; A2A defines how agents invoke each other. Together, they enable complex workflows where an agent uses MCP to fetch data and A2A to ask another agent to analyze it.

A2A vs. Existing Agent Orchestration Frameworks

If you’ve worked with multi-agent systems like LangGraph, AutoGen, or CrewAI, you might wonder how A2A fits in. Those are orchestration frameworks that design how agents collaborate within a single ecosystem.

A2A is different. It’s a communication protocol, not a workflow engine. It doesn’t dictate interaction logic or agent design. Instead, it acts as a global communication system that connects agents across different frameworks.

Think of orchestration frameworks as different offices with their own internal processes. A2A is the universal phone and email system linking all offices. Inside one framework, agents already share a language. But when agents from separate frameworks need to collaborate, A2A bridges that gap without forcing migration.

You might use LangGraph or CrewAI to manage internal agent logic and rely on A2A to communicate beyond those silos. It’s like having a universal email protocol that lets people with different email clients exchange messages seamlessly.

A Hands-On Example: The “Hello World” of Agent2Agent

To illustrate A2A in action, consider a simple "Hello World" agent using the A2A Python SDK.

First, define the agent’s skill and public profile (Agent Card):

from a2a.types import AgentCard, AgentSkill, AgentCapabilities

skill = AgentSkill(
    id="hello_world",
    name="Returns hello world",
    description="Just returns hello world",
    tags=["hello world"],
    examples=["hi", "hello world"]
)

agent_card = AgentCard(
    name="Hello World Agent",
    description="Just a hello world agent",
    url="http://localhost:9999/",
    version="1.0.0",
    defaultInputModes=["text"],
    defaultOutputModes=["text"],
    capabilities=AgentCapabilities(streaming=True),
    skills=[skill]
)

This agent advertises one skill: “hello_world.” It communicates via plain text and supports streaming responses.

Next, implement the logic to respond with "Hello, world!" when asked. The SDK uses an Agent Executor class where you define this behavior.

Then, run the agent as an A2A server using the provided Starlette-based application and Uvicorn web server:

from a2a.server.apps import A2AStarletteApplication
from a2a.server.request_handlers import DefaultRequestHandler
from a2a.server.tasks import InMemoryTaskStore
import uvicorn

request_handler = DefaultRequestHandler(
    agent_executor=HelloWorldAgentExecutor(),
    task_store=InMemoryTaskStore(),
)

server = A2AStarletteApplication(
    agent_card=agent_card,
    http_handler=request_handler
)

uvicorn.run(server.build(), host="0.0.0.0", port=9999)

Running this will launch a live A2A agent at http://localhost:9999. It serves its Agent Card for discovery and listens for task requests through JSON-RPC calls.

You can then use the SDK’s A2AClient class to test communication with this agent.

Getting Started

• Ensure you have Python 3.10 or higher installed.
• Install the A2A SDK using pip install a2a-sdk or an equivalent command.

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