A2A — Agent-to-Agent Protocol

Overview

A2A is an open protocol from Google that defines how AI agents communicate, delegate tasks, and collaborate with each other. While MCP focuses on tool integration, A2A addresses agent-to-agent interoperability.

Core Concepts

Agent Card

Every A2A agent publishes an Agent Card at /.well-known/agent.json describing its capabilities:

{
  "name": "Research Agent",
  "description": "Finds and summarizes information on any topic",
  "url": "https://research-agent.example.com",
  "capabilities": {
    "streaming": true,
    "pushNotifications": true
  },
  "skills": [
    {
      "id": "web-research",
      "name": "Web Research",
      "description": "Search and summarize web content"
    }
  ]
}

Task Lifecycle

A2A communication revolves around Tasks with a defined lifecycle:

submitted → working → [input-required] → completed / failed / canceled

Messages and Parts

Agents exchange Messages containing Parts (text, files, data):

{
  "role": "agent",
  "parts": [
    { "type": "text", "text": "Here is the research summary." },
    { "type": "file", "file": { "name": "report.pdf", "mimeType": "application/pdf", "bytes": "..." } }
  ]
}

Transport

A2A uses HTTP + JSON-RPC 2.0:

• POST / — send JSON-RPC requests
• SSE — streaming responses for long-running tasks
• Push notifications — webhook callbacks for async completion

Key Methods

Example Flow

Client                          Agent
  │                               │
  │── tasks/send ────────────────►│  Create task with user message
  │◄── status: working ──────────│  Agent begins processing
  │◄── status: completed ────────│  Agent returns result
  │                               │

Multi-Agent Collaboration

A client agent can delegate subtasks to specialist agents:

1. Discover agents via their Agent Cards
2. Send tasks to the most suitable agent
3. Receive results and incorporate into the parent task
4. Handle input-required status for interactive collaboration

A2A vs MCP

Best Practices

• Publish an Agent Card with accurate skill descriptions so other agents can discover and route tasks correctly.
• Use streaming (tasks/sendSubscribe) for long-running tasks to provide progress updates.
• Handle the input-required status to support interactive multi-turn workflows.
• Combine A2A with MCP — use A2A for agent-to-agent delegation and MCP for tool integration within each agent.
• Implement idempotency on task IDs so retries don't create duplicate work.