What is the A2A Protocol?

A2A (Agent-to-Agent) is an open protocol by Google that standardizes how AI agents discover and communicate with each other across different frameworks, platforms, and network boundaries. Think of it as HTTP for AI agents — a common language that lets a LangGraph agent talk to a Google ADK agent, or a CrewAI agent delegate tasks to a remote specialist agent.

Last verified: April 2026

Quick Facts

Detail	Info
Type	Open communication protocol
Created by	Google
Purpose	Agent-to-agent communication
Complements	MCP (Model Context Protocol)
Native support	Google ADK
License	Open-source

Why A2A Matters

In April 2026, the AI agent ecosystem is fragmented:

• LangGraph agents can’t natively talk to CrewAI agents
• Google ADK agents can’t delegate to AutoGen agents
• Custom agents are isolated in their own frameworks

A2A solves this by providing a standard protocol for:

1. Agent discovery — Finding available agents and their capabilities
2. Task delegation — Sending tasks to remote agents
3. Authentication — Secure cross-boundary communication
4. Status tracking — Monitoring delegated task progress
5. Result exchange — Getting structured results back

How A2A Works

Agent Cards

Every A2A-compatible agent publishes an “Agent Card” — a JSON document describing its capabilities, endpoints, and authentication requirements. Think of it like an API spec for agents.

{
  "name": "research-agent",
  "description": "Searches the web and summarizes findings",
  "capabilities": ["web_search", "summarization"],
  "endpoint": "https://agents.example.com/research",
  "auth": {"type": "oauth2"}
}

Task Flow

1. Discovery — Client agent reads the remote agent’s Agent Card
2. Task submission — Client sends a structured task request
3. Execution — Remote agent processes the task
4. Status updates — Optional streaming of progress
5. Result — Structured response returned to client

A2A vs MCP: What’s the Difference?

This is the most common question. They solve different problems:

Aspect	A2A	MCP
Connects	Agent ↔ Agent	Model ↔ Tools/Data
Purpose	Agent collaboration	Tool access
Created by	Google	Anthropic
Direction	Bidirectional	Model → Tool
Use case	”Ask the research agent to find data"	"Use the GitHub tool to create a PR”

They’re complementary, not competing. An agent uses MCP to access tools, and A2A to delegate tasks to other agents.

Real-World Example

Imagine a software development pipeline:

1. A planning agent (Google ADK) uses A2A to delegate coding to a coding agent (Claude Code)
2. The coding agent uses MCP to access the GitHub tool and create a PR
3. The planning agent uses A2A to ask a review agent (LangGraph) to review the PR
4. The review agent uses MCP to read the PR diff from GitHub

A2A handles the agent-to-agent coordination. MCP handles the tool access.

Who Supports A2A?

Native Support

• Google ADK — Built-in A2A client and server support
• Vertex AI Agent Builder — Managed A2A infrastructure

Growing Adoption

The protocol is open-source and framework-agnostic. Any agent framework can implement A2A support. As of April 2026, the ecosystem is still early but growing.

When to Use A2A

Use A2A when:

• You need agents from different frameworks to collaborate
• You’re building a distributed agent system across services
• You want to expose your agents as services for others to use
• You need secure, authenticated agent-to-agent communication

You don’t need A2A when:

• All your agents are in one framework (use that framework’s built-in orchestration)
• You’re connecting models to tools (use MCP instead)
• You’re building a single-agent system

The Bottom Line

A2A is early but important. As AI agent systems become more complex and distributed, the need for a standard agent communication protocol is clear. Google’s A2A, combined with Anthropic’s MCP for tool access, is forming the foundation of an interoperable agent ecosystem. If you’re building multi-agent systems that span frameworks or organizations, A2A is worth learning now.