Google's Agent2Agent (A2A) Protocol: A Deep Dive With Real-World Examples

AI agents are transforming the way modern software systems operate—automating tasks, making decisions, and interacting with users autonomously. But as enterprises increasingly adopt agent-based architectures, a fundamental problem arises: how do diverse AI agents, built using different frameworks, communicate and collaborate seamlessly?

Enter Google’s Agent2Agent (A2A) — an open, vendor-neutral protocol designed to enable interoperability between autonomous AI agents, regardless of the platforms they’re built on. In this post, we’ll unpack A2A’s principles, architecture, and real-world applications, and compare it with Google’s Model Context Protocol (MCP) to understand when each shines.

🔍 What Is Agent2Agent (A2A)?

Agent2Agent (A2A) is an open protocol developed by Google to support collaboration between black-box AI agents. These agents may vary in purpose, implementation, or deployment environment—but A2A provides the structure they need to discover one another, share tasks, and exchange information.

A2A Enables Agents to:

• Discover each other dynamically

• Exchange structured tasks and responses

• Stream intermediate results or updates

• Engage in multi-turn conversations

• Operate across modalities (e.g., text, image, audio, structured data)

By abstracting how agents talk to each other, A2A eliminates the tight coupling usually seen in integrated systems. It's particularly useful when agents don’t share memory or tools and must operate independently.

Agent2Agent Protocol Design Principles

The A2A protocol is built around five core principles that emphasize modularity, security, and real-world usability:

1. Agentic by Design

Agents function as autonomous black boxes. They don’t share memory or execution plans. Each agent receives tasks and responds using its own internal logic.

2. Open Standards

A2A leverages existing web protocols for maximum interoperability:

• HTTP for transport

• JSON-RPC 2.0 for messaging

• SSE (Server-Sent Events) for streaming responses

3. Secure by Default

Authentication follows OpenAPI-compatible security schemes, ensuring enterprise-grade protection.

4. Supports Long-Running & Human-in-the-Loop Tasks

Agents can:

• Request additional input from users

• Pause execution

• Stream updates or logs

• Handle asynchronous push notifications via secure webhooks

5. Modality-Agnostic

A2A supports diverse content types:

• Text

• Images

• PDFs

• Audio/video

• Structured formats like JSON or HTML

This flexibility makes it suitable for agents operating across different verticals—from legal tech to IT automation.

---

How Does A2A Work?

A2A defines a structured lifecycle for communication between agents. Here's how it works, step-by-step:

Core Actors

1. User: Triggers the task (e.g., “Fix my laptop”).

2. Client Agent: Formulates the task and sends it to another agent.

3. Remote Agent: Executes the task and provides results.

Agent Discovery via Agent Cards

Agents advertise themselves using an Agent Card, a standardized JSON file (typically hosted at /.well-known/agent.json). The card includes:

• Agent name, version, host URL

• Supported input/output formats

• Skills and use-case tags

• Auth method

• Example payloads

This card enables automated discovery via DNS, registries, or catalogs.

Core Communication Units

• Task: The atomic unit of work. Includes state (submitted, working, input-required, completed).

• Messages: Used for conversation (e.g., clarifications).

• Artifacts: Immutable results (e.g., PDFs, CSVs, JSON blobs).

• Parts: Self-contained data blocks inside messages/artifacts (can be binary, text, or structured).

🔁 Task Lifecycle

1. Client Agent sends task using JSON-RPC.

2. Remote Agent begins execution, possibly requesting input or streaming updates.

3. Task transitions:
   submitted → working → input-required → completed

4. Streaming/async results are sent via SSE or push webhook.

5. Artifacts can be fetched at the end for final output.

A2A Workflow Example: IT Helpdesk Ticket Resolution

Let’s explore a real-world enterprise use case to see A2A in action:

Scenario: User submits a ticket

"My laptop isn’t turning on after the last update."

Step-by-step Breakdown:

1. Client Agent receives the task and sends a structured Task object to:

   • Hardware Diagnostic Agent

     • Runs hardware checks

     • Returns log artifacts

2. If hardware is OK:

   • Escalates to Software Rollback Agent

     • Analyzes OS logs

     • Attempts rollback

3. If rollback fails:

   • Invokes Device Replacement Agent

     • Schedules a new device delivery

     • Streams confirmation and tracking link

Throughout this process:

• Agent Cards enable dynamic discovery

• Tasks encapsulate work and messages

• Artifacts include logs, rollback summaries, etc.

✅ What makes this a pure A2A flow?

• No API tools or OCR involved

• Communication is peer-to-peer between opaque agents

• Native support for async, streaming, and modality-rich interactions

🔄 A2A vs MCP: When to Use What?

While A2A powers peer-to-peer agent collaboration, the Model Context Protocol (MCP) focuses on tool integration and function calling.

Example: Loan Approval Workflow

Step 1: MCP (Tool Interoperability)

• LoanProcessor agent uses MCP to:

  • Fetch credit score

  • Retrieve bank history

  • Validate documents via OCR

Step 2: A2A (Agent Collaboration)

• Collaborates with:

  • RiskAssessmentAgent

  • ComplianceAgent

  • DisbursementAgent

Here, MCP handles structured functions, while A2A handles agent coordination and negotiation.

Conclusion

Google’s Agent2Agent protocol is a powerful step toward modular, scalable, and interoperable multi-agent systems. With built-in support for secure discovery, streaming, modality-agnostic content exchange, and asynchronous workflows, A2A unlocks new possibilities for agent-based enterprise automation.

Whether you're using LangGraph, CrewAI, Google ADK, or your own framework, A2A helps your agents communicate as intelligent peers—not just as passive API consumers.

Further Reading

• CrewAI: A Guide With Examples of Multi-Agent Systems

• Agentic AI: Benefits & Comparison With Traditional AI

• Model Context Protocol (MCP): Architecture & Use Cases