A2A Agent To Agent: Definition and Examples

A2A (Agent-to-Agent) is an open protocol developed by Google that allows autonomous AI agents to communicate, collaborate, and delegate tasks between each other, regardless of their respective frameworks or providers.

Full definition

The A2A (Agent-to-Agent) protocol is an open specification introduced by Google in April 2025 to standardize communication between AI agents. Where each agent traditionally operates in a silo, A2A establishes a common language allowing agents built on different platforms (LangChain, CrewAI, AutoGen, etc.) to interact seamlessly.

A2A's operation is based on several key concepts: Agent Cards (identity cards describing each agent's capabilities), a discovery mechanism to find the most suitable agent for a subtask, and a structured communication protocol based on tasks exchanged via HTTP and JSON. A client agent can thus send a request to a remote agent, track progress, and retrieve the result, all without human intervention.

A2A differs from Anthropic's MCP (Model Context Protocol) in that it handles horizontal collaboration between peer agents, whereas MCP focuses on the vertical connection of an agent to tools and data sources. The two protocols are complementary: an agent can use MCP to access its local tools and A2A to delegate work to other specialized agents.

This protocol fits into the vision of a multi-agent ecosystem where specialized agents (research, writing, data analysis, code execution) collaborate to solve complex problems that no single agent could handle efficiently. Google designed A2A with the support of more than 50 technology partners, including Salesforce, SAP, and Atlassian.

Etymology

A2A is the acronym for 'Agent-to-Agent', modeled on B2B (Business-to-Business) and P2P (Peer-to-Peer) patterns. The term reflects the idea of direct communication between autonomous peers, without human intermediary. The protocol was announced by Google Cloud in April 2025.

Concrete examples

Multi-agent orchestration for complex research

You are a coordinator agent. Use the A2A protocol to delegate financial data research to the specialized agent 'FinanceBot', then transmit the results to the agent 'AnalystBot' to generate a summary report.

Enterprise workflow automation

Configure an A2A system where an HR agent receives leave requests, checks availability via a planning agent, and sends confirmation via an email notification agent.

Dynamic discovery of specialized agents

As a client agent, consult available Agent Cards on the network to find an agent capable of translating a technical document from French to Japanese, then delegate the task via the A2A protocol.

Practical usage

In prompt engineering, understanding A2A allows you to design architectures where multiple specialized agents collaborate on complex tasks. Concretely, you can structure your prompts so that a coordinator agent breaks down a problem and delegates each subtask to the most competent agent. This approach is particularly useful for enterprise workflows involving multiple areas of expertise (legal, technical, commercial).

Related concepts

MCP (Model Context Protocol)Multi-agent systemAgent orchestrationAutonomous agent

FAQ

What is the difference between A2A and MCP?

A2A manages horizontal communication between peer agents (agent-to-agent), while MCP connects an agent vertically to tools and data sources. They are complementary: an agent uses MCP to access its tools and A2A to collaborate with other agents.

Is A2A limited to Google agents?

No, A2A is an open and agnostic protocol. It works with any agent framework (LangChain, CrewAI, AutoGen, Semantic Kernel, etc.) and any language model. More than 50 technology partners support the protocol.

Should A2A be used for all projects involving AI?

No. A2A is relevant when you need to have multiple autonomous agents collaborate, especially in heterogeneous environments. For a single agent using tools, MCP is sufficient. A2A is most meaningful in distributed multi-agent architectures at an enterprise scale.

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