What is A2A? Understanding Google’s Agent2Agent protocol

AI agents are fast becoming part of everyday business—from helping customers online to speeding up hiring. But there’s still one big problem: AI agent silos.

Most agents today are built on different platforms—OpenAI, LangChain, Google Vertex AI, or custom stacks—and they don’t easily talk to one another. It’s like having a team where everyone speaks a different language. As AI takes on a bigger role in operations, these disconnected systems are making collaboration harder and integration costs skyrocket.

Why AI agents don’t play well together

Historically, companies built AI tools in isolation, optimizing them for specific workflows. The result? Great tools that don’t interoperate. Organizations often deploy different agents for HR, logistics, customer service, and sales—but those agents don’t talk to each other.

This fragmentation creates inefficiencies, where tasks get stuck in handoffs or require human intervention just to move data between systems. In a world where businesses compete on speed and automation, this kind of drag can quickly become a bottleneck.

Without a shared protocol, your automation potential hits a ceiling. That’s the issue Google set out to solve.

What is Google Agent2Agent (A2A) protocol?

In April 2025, Google launched the Agent2Agent (A2A) protocol—an open, vendor-neutral framework that lets agents built on different platforms communicate and collaborate.

As Google puts it:

“Enabling agents to interoperate with each other, even if they were built by different vendors or in a different framework, will increase autonomy and multiply productivity gains, while lowering long-term costs.”

Think of A2A as a universal language for AI agents. Instead of custom integrations, A2A provides a common interface that allows agents to discover each other, exchange information, and complete tasks together. Whether it’s a custom-built LLM or a commercially available tool, as long as it adheres to the A2A standard, it can plug into the ecosystem.

Why does Agent2Agent matter?

1. Built on familiar web standards

A2A protocol uses well-known HTTP for sending and receiving requests, JSON-RPC 2.0 for structured messages, and Server-Sent Events (SSE) for real-time updates. These are already widely used in API development, so developers can get started quickly without learning something new.

2. Works across platforms

A2A works with agents built on platforms like OpenAI, Google Vertex AI, LangChain, or even custom large language models. As long as the agent follows the A2A standard, it can work in the same ecosystem—kind of like using a universal translator for AI agents.

3. Supported by a strong community

A2A isn’t just a Google initiative. Over 50 organizations—including Salesforce, SAP, LangChain, Accenture, and McKinsey—are helping build and shape it. That makes A2A a growing industry standard, not just an experimental protocol.

Core components of the A2A protocol

A2A works by giving AI agents a shared framework for how to discover each other, talk, and get things done. Here’s a closer look at the core pieces that make it all work:

1. Defined agent roles

Every A2A interaction involves two roles: a client agent, which sends the request, and a remote agent, which receives the request and completes the task. This clear structure keeps responsibilities organized and easy to manage.

2. Agent discovery with agent cards

Agents share their capabilities through a simple JSON file located at: /.well-known/agent.json

This file works like a resume—it lists what the agent can do, how to contact it, and what formats it supports. Other agents can read this and decide how to interact with it.

3. Unified message format

A2A uses JSON-RPC 2.0, a lightweight format that structures requests and responses clearly. This keeps communication consistent and understandable between any two agents.

4. Flexible communication channels

Agents can communicate using standard web protocols like HTTP for quick requests and Server-Sent Events (SSE) for ongoing, real-time updates. This allows agents to handle everything from instant commands to long-running tasks.

5. Multimodal Support

A2A goes beyond text. It also handles forms, audio and video, and file or image attachments. This makes it well-suited for more interactive workflows and user-facing tools that need richer communication.

6. Task lifecycle management

A2A lets agents track the full progress of a task—from starting it to updating, completing, retrying, or even canceling it. Agents maintain context across the session so they always know what’s going on.

What happens when AI agents use A2A protocol?

Imagine a travel app with an AI assistant designed to handle your airport pickup automatically. Here’s how that experience could work using A2A:

An Uber-scheduling agent checks your flight status and requests a ride.
A Tesla’s onboard agent gets the request and starts preparing the vehicle.
A Google Maps routing agent calculates the best route based on current traffic.
At the same time, a weather agent checks for delays or storms and recommends alternate plans if needed.

These agents come from different companies and are built on different platforms. Normally, they wouldn't be able to communicate without a lot of custom code. But with A2A, they all use the same communication protocol, so they can share updates, pass tasks along, and work together smoothly. The result? Your ride shows up on time, with no extra effort from you.

The problem Google Agent2Agent (A2A) protocol solves

AI agents are powerful, but they often struggle to work together. A2A was designed to solve some of the most common issues holding back multi-agent systems today.

1. Fragmented AI systems

Many organizations use different AI tools for different tasks—like one for customer support, another for scheduling, and another for data analysis. These agents are often built on separate platforms and don’t communicate with each other. As a result, workflows get fragmented, and automation becomes limited or incomplete.

2. High integration costs

To connect these agents, companies usually write custom code or build complex bridges between systems. These one-off solutions are expensive, time-consuming, and hard to scale. As more agents are added, the entire setup becomes more fragile and harder to manage.

3. No common language

Without a shared communication standard, agents remain isolated—each speaking their own language. A2A solves this by giving agents a common way to find each other, understand requests, share context, and complete tasks together, regardless of where they were built or who built them.

Key benefits of A2A protocol

The Agent2Agent (A2A) protocol makes it easier for AI systems to work together. It removes the complexity of connecting agents from different platforms, strengthens security, and helps teams build scalable, flexible solutions.

Here are some of the key benefits A2A brings to the table:

Streamlined communication

A2A uses a unified message format that reduces errors and support issues. Developers can focus on building features instead of fixing integration problems.

Platform independence

With A2A, you can use the best AI tools for the job, no matter who makes them. Agents built on different platforms can work together without extra setup.

Enterprise-grade security

A2A includes built-in support for authentication and access control, making sure only trusted agents can connect and share data.

Support for complex workflows

From submitting forms to exchanging files, video, or other media, A2A enables agents to collaborate on tasks that go beyond simple commands.

Scalability

Because A2A reduces integration work, it’s easier to expand AI systems across teams, departments, or entire organizations without adding more complexity.

The future of Google’s A2A protocol: What’s next and what to watch

Google’s Agent2Agent (A2A) protocol isn’t just another tech tool—it’s a major step toward building a smarter, more connected AI ecosystem. By letting agents communicate across different platforms and frameworks, A2A could change the way businesses automate tasks and scale their AI systems. But like any big innovation, it also brings a few challenges along the way.

Google’s A2A protocol enables:

Real-time collaboration between agents built on different platforms
Seamless integration of multimodal interactions like text, voice, and video
Scalable development of modular, plug-and-play AI ecosystems

SAP highlights that A2A could help eliminate siloed systems, says:

“The A2A protocol represents a significant step beyond simple API integrations... enabling seamless automation across disconnected systems.”

Key challenges that could impact the adoption of A2A protocol

A2A shows a lot of potential, but its long-term success will depend on how well it handles a few important challenges:

Legacy system integration

Many companies still rely on older systems that don’t easily work with modern protocols. Updating these systems to support A2A can be costly and time-consuming, which may slow down adoption.

Security and governance

As agents start sharing more data and working across platforms, strong security becomes essential. A2A needs to offer solid authentication, permission settings, and auditing tools, especially in industries where data privacy is a top priority.

Version compatibility

AI models evolve fast. Without proper version control and backward compatibility, updates could break existing workflows or create confusion between agents. Keeping things consistent across versions is key to avoiding disruptions.

Anil Clifford, founder of Eden Digital, warns:

“Using HTTP and JSON-RPC is practical... but the protocol’s success in handling edge cases in real-world scenarios will determine its proficiency.”

The future of AI collaboration starts with A2A

AI agents aren’t just useful add-ons anymore—they’re becoming the backbone of how modern systems think, respond, and get work done. But without a shared way to communicate, their power is limited.

Google’s Agent2Agent (A2A) protocol provides a practical, open framework that lets agents work together across platforms, vendors, and use cases.

A2A replaces the need for manual handoffs, patchy integrations, and siloed systems with a secure, scalable network of agents that can truly collaborate. It doesn’t just improve how agents talk. It transforms how they work together.

With strong support from the industry and a future-ready design, A2A is more than just a protocol. It's a shift toward smarter, modular, and more connected AI.

At KeyValue, we’re building with A2A at the core — not as an add-on, but as the foundation. Let’s build what’s next.