Agent2Agent protocol (A2A)

Imagine a room filled with brilliant specialists - a doctor, a lawyer, a chef, an engineer - all trying to collaborate on solving a complex problem. Now imagine they all speak completely different languages with no translator in sight. That’s essentially where we are with AI agents today.

Google’s Agent2Agent Protocol (A2A) is trying to solve this Tower of Babel situation. But will it work? And why should we care? Let’s dive in.

The multi-agent communication challenge #

The AI world is moving rapidly from single-purpose models to specialized agents that can perform complex tasks. But there’s a problem: these agents often can’t effectively talk to each other. Each one speaks its own “language” determined by whoever built it and whatever framework they used.

This communication barrier means we’re missing out on what could be one of the most powerful aspects of agent systems: collaboration. It’s like having an incredible team where nobody can understand anyone else.

Google unveiled A2A as an attempt to solve this exact problem. They’ve published a full specification that outlines rules for how agents can communicate with each other and with humans, regardless of their underlying frameworks.

A tale of two protocols: A2A vs. MCP #

You might be wondering how A2A relates to the Model Context Protocol (MCP) we discussed before. They sound similar, but they’re actually complementary:

Think about it this way:

• MCP gives agents access to tools and resources (like search engines or databases)
• A2A gives agents the ability to collaborate with each other

I like to use this analogy: Imagine auto mechanics at a repair shop:

• MCP is how mechanics use diagnostic tools and equipment
• A2A is how mechanics coordinate with each other, talk to customers, and order parts from suppliers

Both protocols are useful, just for different aspects of agent functionality.

The protocol #

So what’s actually in this protocol?

Digital introductions: Agent Cards #

Before agents can collaborate, they need to know what each other can do. A2A uses “Agent Cards” - essentially digital business cards that describe an agent’s capabilities, authentication requirements, and communication interfaces. Think of it as agents exchanging contact information and resumes.

Structured collaboration: Tasks and Messages #

Once agents know about each other, they collaborate through structured Tasks. Each task gets its own unique identifier and carries status information so agents know whether something is in progress, needs more input, or is complete. Tasks also track the messages exchanged between agents and any results produced during their collaboration.

Unlike simple text-based protocols, A2A supports rich content in these messages - things like images and other media types - which turns out to be crucial when agents need to exchange complex information.

How agents discover each other #

One of the clever aspects of A2A is how it handles agent discovery. The protocol supports several approaches to finding agents.

There’s a simple web directory model where companies publish their agent details at a predictable URL (something like https://DOMAIN/.well-known/agent.json), making them easy to find - similar to how web services use .well-known URIs for service discovery or how OpenAPI documents are published. For more controlled environments, organizations can maintain their own curated catalogs - essentially internal “app stores” of approved agents. And when privacy or security is paramount, companies can implement custom discovery systems with appropriate access controls.

This flexibility allows for both open innovation and controlled enterprise deployment - a smart balance.

For builders #

If you’re building AI systems, A2A opens up interesting architectural possibilities. You might find yourself breaking down complex applications into specialized agent components that communicate through a standard protocol. Or perhaps you’ll integrate capabilities from third-party agents without having to reimplement their functionality. The real power might be in creating extensible systems where you can add new capabilities simply by connecting new specialized agents.

If you’re curious about implementation details, the A2A GitHub repo contains examples using different frameworks like Google ADK, LangGraph, and Crew.AI.

Does A2A actually matter? #

Some tech standards stick around, while others are just pit stops on the way to something better.

What’s clear is that we need some way for AI agents to talk to each other. As agents get smarter and more specialized, the benefits of having them work together become too big to ignore.

If A2A catches on, we might see specialized agent teams forming, multi-vendor ecosystems developing, and users talking to one main agent that handles all the behind-the-scenes coordination.

Is A2A the final answer? Probably not in its current form. But just like we saw with RAG patterns, the AI community tends to converge around good ideas.

For now, it’s a neat development worth watching - especially if you’re working with agents yourself.