The Agent2Agent (A2A) protocol is Google's bold new step toward a future where AI agents from different companies and platforms can work together without human input. Discover how this open-source communication standard is creating a connected ecosystem of bots that handle tasks across business tools—and what it could mean for the future of work. At 3minread.com, we break down the trends shaping AI, crypto, and the web in three minutes or less.

What Is an AI Agent—and Why Are They the Future of Automation?

AI agents are autonomous tools designed to get things done—no babysitting required.

In today’s rapidly evolving tech landscape, AI agents are emerging as a core concept. An AI agent is essentially an autonomous tool that you can task with a goal—and it executes that goal on its own. Whether it’s gathering research, scheduling meetings, booking flights, or running backend operations, these agents are trained to think and act with minimal human input.

Think of ChatGPT’s web research features or Zapier’s automation bots—these are early examples of agents. You give them a task, and they move independently to complete it. But here’s the problem: while these agents are getting smarter, most still operate in silos. One tool can't directly communicate with another unless it's been specifically built to do so.

This is where Google’s A2A protocol enters the picture. It’s not just a new tool—it’s a proposed universal language for agents to talk to each other. And if it works as intended, it could eliminate one of the biggest limitations in enterprise automation: disconnected systems.

What Is the A2A Protocol and Why Is Google Betting on It?

The Agent2Agent (A2A) protocol is Google’s open-source effort to create a shared communication framework for AI agents.

A2A, or Agent2Agent, is a brand-new protocol developed by Google to let AI agents from different companies and platforms communicate seamlessly. Instead of forcing every business to custom-code integrations between tools, A2A provides a standardized, open-source foundation for inter-agent dialogue.

Imagine asking your AI scheduling assistant to book a business trip. With A2A in place, that assistant could reach out to a travel booking agent, coordinate with your expense management system, update your calendar, and send notifications to your team—all without any hard-coded bridges or manual effort. The agents would simply talk to each other using A2A.

The initiative is already backed by major players like Atlassian, Salesforce, ServiceNow, Box, and Workday. Each of these partners sees the value in building a network where AI can independently manage workflows across platforms.

The brilliance of A2A lies in its simplicity and extensibility. Agents are categorized as either client (the one making a request) or remote (the one executing the task). A client agent can delegate a job—like posting a job listing or conducting a background check—to a remote agent specialized in that function.

These jobs can be immediate or involve long-running processes. If more time is needed, the remote agent provides updates until the task is complete. This structure means tasks can be nested, sequenced, and intelligently managed without human oversight.

How the A2A Protocol Works Under the Hood

A2A is built on standard web technologies, making it easy to adopt and secure enough for enterprise use.

Underneath the hood, A2A relies on battle-tested web protocols like HTTP, JSON, and Server-Sent Events (SSE). That means developers can plug it into existing stacks without having to reinvent the wheel. It also supports robust security, with enterprise-grade authentication baked in from the start.

Each A2A-compatible agent includes an agent card—a kind of digital resume that lists its capabilities, endpoints, and supported features. When one agent wants to delegate a task, it consults this card to understand how to format its request and what functions are available.

Communication happens through messages, made up of individual parts (data packets that could include text, images, or structured content). The final output of a task is called the artifact—a document, a decision, or any other deliverable produced by the remote agent.

This framework makes the protocol both flexible and scalable. Whether the agents are handling quick transactional tasks or complex workflows that unfold over hours or days, A2A accommodates both use cases.

And while it may sound abstract, the protocol has very real implications. In Google's hiring workflow demo, for instance, one chatbot uses A2A to trigger agents that post job ads, source candidates, schedule interviews, and verify backgrounds. While this example is hypothetical for now, it illustrates how multi-agent collaboration could function in the near future.

A2A vs. MCP: How Google’s Protocols Work Together

A2A handles the communication; MCP handles the data—together, they form a powerful automation duo.

If you’ve heard of Google’s Model Context Protocol (MCP), you might be wondering how it compares to A2A. While both are aimed at improving AI interoperability, they serve different (but complementary) roles.

• A2A enables agents to talk to each other. It handles messaging, task delegation, and multi-agent coordination.
• MCP allows agents to talk to external tools. It’s about granting agents access to databases, APIs, and other traditional data systems.

In many scenarios, an AI agent would use MCP to fetch the data it needs (like pulling your travel history from a CRM), and then A2A to delegate that data to another agent (like a booking tool or travel assistant). It’s a one-two punch that allows for both data-rich insights and smart execution.

Together, these protocols lay the groundwork for a future in which AI agents can seamlessly collaborate, adapt, and solve business challenges faster than any single app or human could.

Build Your Own AI Agent—and Connect It to the Future

You don’t need Google’s resources to start building useful AI agents—tools like Zapier are already making it possible.

While A2A is still in its early days, the vision it outlines is already attainable at smaller scales. Zapier Agents, for instance, allow you to build your own custom AI-powered assistants using natural language prompts. These agents can monitor your inbox, update CRMs, generate reports, and complete hundreds of other tasks by integrating with over 6,000 apps.

What sets the current generation of agents apart is accessibility. You don’t need to write Python or spin up servers—just describe what you want, and Zapier’s tools help bring it to life. As protocols like A2A gain traction, these agents will become even more powerful, able to plug into an entire web of intelligent tools across vendors.

The takeaway? We’re moving from a world of siloed apps to one where AI agents manage entire workflows on your behalf. Whether you’re running a startup or managing enterprise-scale systems, getting familiar with agent-based automation today will give you a major advantage tomorrow.