Artificial Intelligence is evolving fast, with agents now capable of handling complex tasks. But what happens when these agents need to work together? How can an AI created by one company collaborate with another, or even with other agents in the same system?

Recently, Google introduced the Agent-to-Agent (A2A) protocol—a major step toward improving collaboration between AI systems. This post explores what A2A is, why it matters, and how it works.

Why AI Agents Struggle to Work Together

Picture this: You use an advanced AI agent to manage your travel plans. It finds flights and hotels efficiently—but when it needs to coordinate with a separate tour-booking agent or check your company’s travel policy via an HR agent, things fall apart. That’s because most AI agents operate in silos. Integrating them often requires custom solutions, which can break if one agent changes its interface.

The Model Context Protocol (MCP) helps standardize how agents interact with tools and services—but it doesn’t cover how agents talk to each other. That’s the gap A2A aims to fill.

What Is the A2A Protocol?

Google’s A2A protocol provides a common framework for AI agents to communicate and collaborate—regardless of who built them or how they work. Think of it like how web browsers and servers use HTTP to communicate. With a shared standard, agents no longer need custom integrations to interact.

In essence, A2A is designed to be a universal translator for AI agents.

How A2A Works

A2A uses simple, widely adopted web standards. It has two core components:

1. Agent Discovery

To find and query other agents, A2A uses a discovery mechanism similar to OpenID Connect. Each agent host can publish a JSON file at a well-known URL:

https://your-agent-domain.com/.well-known/agent.json

This file describes the agent—its name, capabilities, interaction endpoint, organization, version, and skills.

2. Standardized Communication

Agents interact over familiar technologies like HTTP and JSON-RPC. This allows them to exchange messages, share context, request actions, or transfer files—all without needing new infrastructure.

How A2A and MCP Work Together

A2A doesn’t replace MCP—they complement each other. Imagine a user interacting with a Zomato agent to order food. Here’s how A2A and MCP come into play:

• The Zomato agent finds a Pizza Hut agent using its agent.json.
• It queries Pizza Hut for menu items via A2A.
• It also contacts a McDonald’s agent for burger options.
• Each food agent may then use MCP to fetch updated menu data from their own databases.
• All responses are sent back to the Zomato agent via A2A, which then shows options to the user.

This setup allows specialized agents to focus on their tasks while still contributing to a broader, coordinated experience.

Why A2A Matters

A2A moves us closer to true interoperability among AI systems. It lets agents—built by different teams and using different technologies—work together smoothly. This enables more complex workflows and avoids the limitations of siloed systems.

By using open standards, A2A also reduces the risk of vendor lock-in and encourages wide community adoption.

What’s Next for A2A?

With support from over 50 partners—including Langchain and Atlassian—A2A is off to a strong start. While it's still early days, the potential impact is significant.

Much like how OpenID Connect standardized identity management online, A2A could do the same for AI agent collaboration. We may soon see a future where AI agents routinely cooperate to solve problems, assist users, and power complex systems.

In Closing

The A2A protocol marks a big step toward smarter, more connected AI. As adoption grows, we’ll see agents move beyond individual tasks to become part of collaborative systems that deliver more intelligent and seamless experiences.

If you’re interested in the future of AI and collaboration technologies, A2A is something to watch closely as it continues to evolve.