Google's A2A Protocol: How AI Agents Will Communicate in 2025

Google has made a significant move in the AI space with the announcement of several agent-related products and services, indicating their vision that 2025 will be the year of AI agents. Among these announcements, the Agent-to-Agent (A2A) protocol stands out as a particularly important development that could shape how AI systems interact in the future.

Google's New AI Agent Ecosystem

Before diving into the A2A protocol specifically, it's worth noting the suite of agent-related tools Google has introduced:

Agent Space: A hub for enterprises to connect various data sources to different agents for AI-assisted searching and processing
Agent Development Kit: A Python SDK for building agentic applications, positioned as an alternative to langraph and langchain
Firebase Studio: An AI-powered web application creator that uses natural language to build applications

Firebase Studio allows developers to create web applications using natural language instructions, showcasing Google's push toward AI-powered development tools

What Is the A2A Protocol?

The Agent-to-Agent (A2A) protocol is Google's answer to a future where multiple specialized AI agents need to communicate and collaborate. Rather than creating one monolithic agent that attempts to do everything (often poorly), the A2A protocol enables specialized agents to work together, each handling tasks they're optimized for.

The fundamental idea is similar to how we use different applications for different tasks today. Just as we might use specialized software for photo editing, document creation, and communication, we'll likely have specialized AI agents for different purposes in the future.

Why Multiple Specialized Agents Instead of One Super Agent?

Complexity management: Different tasks require different approaches
Security considerations: Limiting what each agent can access reduces risk
Avoiding vendor lock-in: Using agents from different providers prevents dependency on a single ecosystem
Model optimization: Different AI models excel at different tasks (coding, writing, data analysis, etc.)
Fine-tuning efficiency: Specialized agents can use models fine-tuned for specific domains

The A2A protocol enables independent AI agents to communicate seamlessly, allowing them to collaborate on complex tasks while maintaining their specialized capabilities

How A2A Differs from the Model Context Protocol (MCP)

Google positions the A2A protocol as complementary to Anthropic's Model Context Protocol (MCP), not as a replacement. Understanding the distinction helps clarify their respective roles:

MCP focuses on standardizing how tools are exposed to a single agent, making it easier to add capabilities to an individual agent
A2A focuses on standardizing how independent agents communicate with each other, enabling collaboration between specialized agents
Both can work together: An agent might use MCP to access tools directly, while using A2A to delegate tasks to other specialized agents

A practical example: Your primary agent might need to interact with GitHub. It could either use GitHub's MCP tools directly or connect to a specialized GitHub agent (via A2A) that's fine-tuned for code-related tasks and GitHub-specific operations.

The Technical Architecture of A2A

The A2A protocol includes several crucial components that enable agent-to-agent communication:

1. Agent Discoverability via Agent Cards

For agents to communicate effectively, they need to understand each other's capabilities. This is accomplished through "agent cards" - JSON documents that describe:

The agent's capabilities and specializations
Authentication requirements (if any)
Available communication mechanisms (e.g., server-sent events, push notifications)
API endpoints and interaction patterns

JSON

{
  "agent": {
    "name": "GitHub Assistant",
    "version": "1.0",
    "capabilities": ["code-review", "repository-management", "issue-tracking"],
    "authentication": {
      "required": true,
      "type": "oauth2",
      "provider": "https://github.com/login/oauth"
    },
    "endpoints": {
      "task": "/api/v1/tasks",
      "status": "/api/v1/status"
    }
  }
}

2. Authentication Mechanisms

When one agent needs to communicate with another that requires authentication, the protocol defines how this process should work:

The client agent reads the authentication requirements from the target agent's card
The client agent obtains the necessary credentials (e.g., session token) from the specified provider
The client agent includes these credentials when making requests to the target agent
The target agent validates the credentials before processing the request

The A2A protocol includes standardized authentication workflows, allowing agents to securely establish trust before sharing sensitive information or capabilities

3. Task Description and Communication

Once authenticated, agents communicate by sending task descriptions. These structured requests include:

Session IDs for tracking conversations
Detailed descriptions of the requested task
Any necessary context or data for completing the task
Preferences for how the task should be handled

JSON

{
  "session": "abc123",
  "task": {
    "type": "code-review",
    "description": "Review pull request #42 for security vulnerabilities",
    "context": {
      "repository": "organization/project",
      "pull_request": 42
    },
    "preferences": {
      "detail_level": "high",
      "focus_areas": ["security", "performance"]
    }
  }
}

The Future of AI Agent Ecosystems

The A2A protocol represents an important step toward a future where AI agents form complex ecosystems, similar to how web services and APIs work together today. While it's too early to predict whether A2A, MCP, or some other standard will ultimately dominate, the trend toward specialized, interconnected agents seems clear.

This approach offers several advantages for developers and users:

More powerful AI systems through specialized components
Greater flexibility in choosing the right tools for specific tasks
Reduced dependency on single vendors or models
Ability to leverage domain-specific fine-tuning
Improved security through compartmentalization

Getting Started with A2A

For developers interested in exploring Google's agent ecosystem, the Agent Development Kit (ADK) provides a Python SDK for building agents that can communicate using the A2A protocol. Combined with Agent Space for enterprise deployments, these tools offer a pathway to creating interconnected agent systems.

As we move toward 2025 and beyond, understanding protocols like A2A will likely become increasingly important for developers working in the AI space. Whether building specialized agents or creating systems that orchestrate multiple agents, the ability to leverage standardized communication protocols will be a valuable skill in the evolving AI landscape.

Understanding Google's A2A Protocol: The Future of AI Agent Communication