A2A Protocol Explained: How Google's Agent-to-Agent Standard Works (2026)

AI agents are proliferating. Your team might use Claude Code for development, a GPT-powered agent for research, and a Gemini agent for data analysis. The problem: these agents cannot talk to each other. Each one operates in isolation, which means you manually copy-paste outputs between tools.

Google's A2A (Agent-to-Agent) protocol fixes this. It is an open standard that lets AI agents discover each other, negotiate capabilities, and collaborate -- regardless of which platform built them or which LLM powers them.

This guide covers what A2A is, how it works under the hood, how it compares to Anthropic's MCP, and what it means for teams running multi-agent workflows.

Related guides: MCP Servers Guide · AI Agent Orchestration Guide · Build an AI Agent in 5 Minutes

What Is the A2A Protocol?

A2A is an open protocol (now a Linux Foundation project) that defines how AI agents communicate with each other. Think of it as HTTP for agents -- a standard language that lets any agent talk to any other agent, even if they were built by different companies on different frameworks.

Key facts:

• Created by Google, open-sourced in 2025
• Now governed by the Linux Foundation
• Version 1.0 released March 2026
• SDKs available in Python, Go, JavaScript, Java, and .NET
• 23,000+ GitHub stars, 560+ contributions

The protocol solves a specific problem: agent interoperability. Without A2A, integrating two agents requires custom glue code for every pair. With A2A, any compliant agent can work with any other compliant agent out of the box.

How A2A Works: The Architecture

A2A defines three core concepts:

1. Agent Cards

Every A2A-compliant agent publishes an Agent Card -- a JSON document that describes what the agent can do. This is similar to an OpenAPI spec but for agents.

{
  "name": "research-agent",
  "description": "Deep research agent powered by GPT-4",
  "capabilities": ["web_search", "summarize", "cite_sources"],
  "authentication": {"schemes": ["api_key"]},
  "endpoints": ["https://api.example.com/a2a"]
}

When an agent wants to find collaborators, it reads Agent Cards to discover what other agents can do.

2. Tasks

Agents communicate through Tasks. A Task is a unit of work that one agent delegates to another. Tasks have a defined lifecycle:

1. Submitted -- Client agent sends a task request
2. Working -- Remote agent is processing
3. Completed -- Task is done, results are returned
4. Failed -- Something went wrong

Tasks support streaming (for long-running work), artifacts (for file/image outputs), and status updates.

3. Message Passing

Agents exchange Messages that contain Parts (text, files, structured data). A message might look like:

Agent A → Agent B:
  "Research the top 5 A2A implementations and return structured data"
  
Agent B → Agent A:
  [Structured JSON with 5 entries, each with name, stars, language, and status]

This message-passing model is intentionally simple. It works over HTTP and does not require persistent connections or complex state management.

A2A vs MCP: What Is the Difference?

A2A and MCP (Model Context Protocol) are complementary, not competing. They solve different problems:

In practice: You use MCP to connect an agent to your GitHub repos, Slack, and database. You use A2A to let that agent delegate subtasks to other agents.

A real multi-agent workflow uses both:

• MCP connects each agent to tools and data sources
• A2A lets agents coordinate and hand off work to each other

Setting Up A2A: Quick Start

Here is a minimal A2A agent in Python using the official SDK:

from a2a.server import A2AServer, AgentCard, Skill

card = AgentCard(
    name="summarizer",
    description="Summarizes text concisely",
    skills=[
        Skill(id="summarize", name="Summarize Text")
    ]
)

server = A2AServer(agent_card=card)

@server.on_task("summarize")
async def handle_summarize(task):
    text = task.message.parts[0].text
    summary = await your_llm_call(text)
    return {"summary": summary}

server.run(port=8000)

And a client that discovers and calls this agent:

from a2a.client import A2AClient

client = A2AClient()

agents = await client.discover("summarizer")
result = await agents[0].send_task("summarize", {
    "text": "Long article about AI agents..."
})
print(result.summary)

This takes under 10 minutes to set up.

What A2A Means for Multi-Agent Workflows

A2A unlocks a specific pattern: cross-provider agent squads. Instead of picking one LLM provider, you can:

1. Run a Claude-powered agent for nuanced analysis
2. Run a GPT-4 agent for code generation
3. Run a Gemini agent for multimodal tasks
4. Let them coordinate through A2A

This is the architecture we use at Ivern AI. Agents from different providers collaborate on tasks, each handling what it does best. A2A provides the communication layer.

BYOK and A2A Together

In a BYOK (Bring Your Own Key) model, A2A is especially powerful because:

• You are not locked into one provider's agent ecosystem
• You can mix agents from Claude, OpenAI, Google, and open-source models
• Each agent uses your own API keys -- no intermediary markup
• A2A handles the coordination layer regardless of which provider powers each agent

Real-World A2A Use Cases

1. Research Pipeline

A research agent receives a query, breaks it into subtopics, and delegates each to specialized agents:

Research Coordinator (GPT-4)
  → Market Research Agent (Claude)
  → Technical Analysis Agent (Gemini)
  → Citation Agent (GPT-4)
  → Report Writer Agent (Claude)

Each agent runs independently and reports back. The coordinator assembles the final report.

2. Code Review System

A code review agent receives a pull request and coordinates:

Code Review Coordinator
  → Security Scanner Agent
  → Style Checker Agent
  → Test Generator Agent
  → Documentation Agent

3. Content Production

A content pipeline where agents handle different stages:

Content Director
  → Research Agent (gathers data)
  → Writer Agent (drafts content)
  → Editor Agent (reviews and refines)
  → SEO Agent (optimizes titles and meta)
  → Distribution Agent (formats for each platform)

Current Limitations

A2A is young. As of May 2026:

• Adoption is early -- Major platforms (LangChain, CrewAI, AutoGen) have experimental support but not full integration
• Security model is basic -- API key authentication is supported; OAuth and mTLS are in draft
• No built-in state management -- Agents must handle their own state persistence
• Discovery is manual -- No global agent registry yet; you must know agent endpoints or use a custom directory

These limitations will likely be addressed in v2.0, expected late 2026.

Getting Started with A2A

If you want to experiment with A2A today:

1. Install the SDK: pip install a2a (Python) or npm install @a2a/sdk (JavaScript)
2. Read the spec: github.com/google/A2A
3. Build two simple agents and make them communicate
4. Try a cross-provider setup -- one agent on Claude, one on GPT-4

For teams that want cross-agent coordination without building the infrastructure themselves, Ivern AI provides managed multi-agent squads with built-in A2A-compatible communication. You bring your own API keys and deploy coordinated agent teams through a web interface.

Key Takeaways

• A2A is an open protocol for agent-to-agent communication, created by Google
• It complements MCP (which connects agents to tools, not to other agents)
• Version 1.0 is production-ready with SDKs in 5 languages
• It enables cross-provider agent squads where different LLMs collaborate
• BYOK platforms benefit most because they are not locked into one provider's ecosystem
• The protocol is early but moving fast -- now is the time to experiment

Try it: Build your first multi-agent squad with Ivern AI -- free tier, no credit card required.