What is A2A (Agent to Agent Protocol)?

Agent to Agent Protocol (A2A) is a standardized communication framework that enables AI agents to interact with each other in a consistent, predictable way. Released by Google and adopted by various companies including Intuitive, LangChain, and MongoDB, A2A provides the infrastructure for different AI agents to collaborate effectively on complex tasks.

Core Concepts of A2A Protocol

What A2A Solves

In modern AI applications, complex tasks often require the expertise of multiple specialized agents. For example, planning a trip might involve agents specializing in flight booking, hotel recommendations, and local activities. Without a standardized protocol, each of these agents would need custom integration code to communicate with each other, leading to:

1. Inconsistent communication patterns
2. Incompatible data formats
3. Difficult maintenance and updates
4. Limited interoperability

A2A solves these issues by establishing standard ways for agents to:

• Discover each other’s capabilities
• Exchange information
• Process tasks collaboratively
• Handle errors and exceptions

A2A vs. MCP (Model Context Protocol)

While these protocols might seem similar, they serve different purposes:

• MCP (Model Context Protocol): Focuses on how a language model communicates with tools and resources (like APIs, databases, and knowledge sources). Think of MCP as how a mechanic interacts with their tools for more context you can read here.
• A2A (Agent to Agent Protocol): Focuses on how complete agents (each containing an LLM plus tools) communicate with each other. Think of A2A as how a mechanic communicates with customers or parts suppliers.

A useful analogy: If an agent is like a car repair person (LLM) with their toolbox (tools connected via MCP), then A2A defines how this car repair person communicates with customers, parts suppliers, and other specialists.

Key Components of A2A

1. Agent Cards

Agent cards are standardized descriptions that agents use to advertise their capabilities to other agents. Think of an agent card as a business card or profile that includes:

Example of an Agent Card (JSON format):

{
  "name": "Google Maps Agent",
  "description": "An agent that helps with map-related tasks",
  "url": "https://maps-agent.example.com",
  "provider": {
    "name": "Google",
    "url": "https://google.com"
  },
  "capabilities": [
    {
      "type": "route_planning",
      "description": "Plan routes between locations"
    },
    {
      "type": "custom_map",
      "description": "Create custom maps with points of interest"
    }
  ]
}

2. Agent Discovery

For agents to collaborate, they first need to discover each other. A2A supports multiple discovery methods:

• DNS-based Discovery: Clients can discover agents by resolving domain names to find agent.json files.
• Registry-based Discovery: Applications maintain registries of trusted agents.
• Private Discovery: Direct configuration of known agent endpoints.

3. Task Processing Flow

The A2A protocol defines a standard flow for task processing between agents:

Tasks in A2A follow a standard format, typically expressed in JSON:

{
  "task": {
    "input": "Tell me a joke",
    "instructions": "Generate a short, family-friendly joke"
  }
}

4. Communication Protocols

A2A is built on established communication protocols:

• HTTP/HTTPS: For basic request/response communication
• JSON-RPC: For structured method calls
• Server-Sent Events (SSE): For streaming responses
• JSON: As the standard data exchange format

5. Advanced Features

A2A supports several advanced features for robust agent interaction:

• Multimodal Communication: Handling text, images, audio, and other data types
• Streaming: Real-time streaming of responses for long-running tasks
• Error Handling: Standardized error formats for better debugging
• Clarification Requests: Ability for agents to ask follow-up questions

Practical A2A Implementation

Let’s examine how A2A looks in a practical implementation:

Example: Reimbursement Agent

// Agent Card Definition
const agentCard = {
  name: "Expense Reimbursement Agent",
  description: "An agent that processes expense reimbursement requests",
  url: "https://reimbursement.example.com",
  provider: {
    name: "Financial Services Inc.",
    url: "https://financial-services.example.com"
  },
  capabilities: [
    {
      type: "process_reimbursement",
      description: "Process expense receipts and generate reimbursement forms"
    }
  ]
};

// Agent Implementation
class ReimbursementAgent {
  constructor() {
    this.llm = new GeminiModel("gemini-2.0-flash");
    this.tools = [
      new ReceiptParser(),
      new PolicyValidator(),
      new FormGenerator()
    ];
  }
  
  async processTask(task) {
    // Process the task using LLM and tools
    const parsedReceipt = await this.tools[0].execute(task.input);
    const validationResult = await this.tools[1].execute(parsedReceipt);
    
    if (validationResult.needsClarification) {
      return {
        type: "clarification_request",
        question: validationResult.question
      };
    }
    
    const reimbursementForm = await this.tools[2].execute(validationResult);
    return {
      type: "task_complete",
      result: reimbursementForm
    };
  }
}

Real-World A2A Workflow Example

To better understand A2A in action, let’s walk through a real-world example:

In this example:

1. An HR manager interacts with a client agent in Google Agent Workspace
2. The client agent discovers and communicates with specialized agents for candidate sourcing and interview scheduling
3. Agents ask clarifying questions when needed
4. Each agent performs its specialized function using its own tools and resources
5. The client agent coordinates the entire workflow and presents results to the user

Benefits of A2A Protocol

1. Standardized Communication: Consistent way for agents to interact without custom integration
2. Composability: Easy to combine specialized agents for complex tasks
3. Discovery: Mechanisms to find available agents and their capabilities
4. Interoperability: Agents from different providers can work together
5. Extensibility: New agent types can join the ecosystem

Current Status and Future Potential

Agent to Agent Protocol is still in its early stages, having been recently released by Google. While many companies have already begun adoption, the full impact and evolution of the protocol will unfold over time.

Key areas of development include:

• Security and authentication standards
• More sophisticated agent discovery mechanisms
• Advanced error handling and recovery
• Cross-platform agent compatibility
• Integration with existing AI ecosystems

Conclusion

Agent to Agent Protocol (A2A) represents a significant step toward creating a more collaborative AI ecosystem. By standardizing how agents communicate with each other, A2A enables more complex, multi-agent solutions to emerge without requiring custom integration for each agent pairing.

As the AI landscape continues to evolve with specialized agents for different domains, A2A provides the communication infrastructure that will allow these agents to work together seamlessly, ultimately enabling more powerful and flexible AI applications.