As artificial intelligence evolves, we are entering an era where autonomous agents no longer function in isolation—they collaborate. Google’s recently introduced Agent-to-Agent (A2A) Protocol is a major step toward making this collaborative future a reality. Designed as an open standard for agent communication, A2A is already supported by over 50 industry leaders, including Salesforce, PayPal, Atlassian, and SAP.

In this blog post, will break down the A2A Protocol, explore how it differs from Anthropic’s Model Context Protocol (MCP), examine its architecture, and look at real-world applications. If you’re a developer, product architect, or just AI-curious—this is your guide to the next wave of interoperable agent ecosystems.

What is A2A Protocol?

The Agent-to-Agent (A2A) Protocol is an open communication framework developed by Google to enable secure, direct, and scalable communication between autonomous AI agents. These agents can belong to different organizations, systems, or services, and yet collaborate as part of a larger workflow.

Key Features:

• Direct agent messaging across systems.
• Secure communication and authentication.
• Designed for cross-platform task execution.
• Enterprise-ready (compatible with Salesforce, Workday, SAP, etc.)

Think of A2A as a universal language for AI agents to talk to one another, securely and meaningfully.

How A2A Differs from MCP

In essence:

MCP is about internal integration.
A2A is about external cooperation.

Architecture of A2A: How It Works

The A2A Protocol provides a clean and modular framework to enable agents to discover, connect, and cooperate. Here’s a high-level breakdown of its architecture:

Components:

Client Agent: The initiator agent that starts the task (e.g., “Plan my trip”)
Remote Agents: Specialized agents for individual services (e.g., hotel booking)
Agent Discovery: Uses a JSON-based registry (agent.json) to find available agents
Configuration Files: Each agent exposes metadata like name, capabilities, and endpoints

Use Case: Travel Planning with A2A

Imagine a user gives a request:

“Plan my trip to Europe with the lowest cost possible.”

Here’s how the A2A-enabled system responds:

Step 1: Agent Discovery:

• The client agent scans the agent.json file to find relevant services:
• Emirates and Air India for flights
• Taj and Marriott for hotels

Step 2: Secure Communication:

• Each agent communicates securely using protocols defined in their config files.

Step 3: Task Coordination:

• Flight agents compare prices and seat availability
• Hotel agents check for deals

Step 4: Booking Execution:

• Agents collaborate, finalize the cheapest and best itinerary, and execute bookings autonomously.

Conclusion

Google’s A2A Protocol represents the shift in how AI agents operate—not as isolated bots, but as collaborative digital workers. By enabling secure, interoperable communication across platforms, A2A opens the door to smarter, more seamless AI-driven applications.

As the AI landscape continues to evolve, mastering protocols like A2A and MCP will be essential for developers, enterprises, and innovators alike.

Sanjay N