How A2A Protocol Works

Most AI agents today work in isolation. They can use tools, call APIs, and follow instructions, but they can't talk to other agents. If you want Agent A (say, a research agent) to hand off work to Agent B (a writing agent), you're building that plumbing yourself. Google's Agent-to-Agent (A2A) Protocol is an attempt to standardize that plumbing.

What A2A actually does

A2A defines a common way for AI agents to discover each other, exchange messages, and coordinate tasks. Think of it as HTTP for agent-to-agent communication -- a shared protocol so agents built by different teams (or different companies) can interoperate without custom integration code.

How it works

The protocol has a few layers:

• Discovery: Agents publish "Agent Cards" describing what they can do. Other agents read these cards to figure out who can help with what.
• Task management: One agent can create a task, assign it to another agent, and track its status. Tasks have defined lifecycles (submitted, working, completed, failed).
• Messaging: Agents exchange structured messages within the context of a task. Messages can contain text, files, or structured data.
• Streaming: For long-running tasks, agents can stream partial results back using Server-Sent Events.

Where it fits: A2A vs MCP

A2A and Anthropic's Model Context Protocol (MCP) solve different problems:

• MCP is about how an agent uses tools and accesses data. It's vertical -- connecting an agent downward to capabilities.
• A2A is about how agents talk to each other. It's horizontal -- connecting agents to other agents.

You'd use both in a real system. MCP lets your agent query a database or call an API. A2A lets your agent delegate a subtask to a specialized agent running somewhere else.

Practical use cases

• Multi-vendor agent systems: Your company uses a Claude-based research agent and a GPT-based coding agent. A2A lets them coordinate on a task without you writing glue code.
• Agent marketplaces: Agents advertise their capabilities via Agent Cards. A travel-planning agent discovers and delegates to a flights agent, a hotels agent, and a local-activities agent.
• Enterprise workflows: A compliance agent reviews documents flagged by a monitoring agent. The handoff follows a standard protocol instead of a custom webhook chain.

What's missing

A2A is still early. A few gaps:

• Authentication and trust between agents isn't fully specified. In practice, you need to decide which agents are allowed to talk to yours.
• Error semantics are basic. When an agent fails mid-task, the recovery story is "publish a failed status." Real distributed systems need more nuance.
• Adoption is thin. As of mid-2025, most multi-agent setups use custom orchestration (LangGraph, CrewAI, Autogen) rather than A2A.

Should you use it?

If you're building a system where agents from different frameworks or vendors need to collaborate, A2A is worth watching. The protocol was donated to the Linux Foundation alongside MCP, which suggests both Google and Anthropic are serious about making these open standards.

For most projects today, you'll get more mileage from a framework like LangGraph or CrewAI for multi-agent orchestration. But if you're designing for a future where agents are independently deployed services that discover and call each other -- more like microservices than function calls -- A2A is the closest thing to a standard we have.