In April 2025, at its Cloud Next conference, Google introduced something that might fly under the radar for most, but it’s exactly the kind of infrastructure upgrade that makes the AI ecosystem more useful, open, and interoperable. It’s called the Agent2Agent protocol, or A2A.

What Is the Agent2Agent (A2A) Protocol?

The Agent2Agent Protocol is an open technical standard created by Google to let AI agents, even those built by different vendors, talk to each other, delegate tasks, and share results, securely and efficiently.

Get to know the Agent2Agent Protocol (A2A), an open protocol that provides a standard way for agents to collaborate with each other, regardless of underlying framework or vendor.

A2A complements Anthropic’s Model Context Protocol (MCP) → https://t.co/p9ZabjHLnf #GoogleCloudNext pic.twitter.com/Z4JlCin6hB

— Google Cloud Tech (@GoogleCloudTech) April 10, 2025

To put it simply: A2A is what lets an AI assistant built by Google coordinate with an AI assistant from Salesforce, SAP, or a custom in-house model without needing brittle, custom integrations or backchannel workarounds.

Now to understand such systems, you must learn about AI agents first. For coders and non-coders alike, the Certified Agentic AI Expert™ is a great starting point.

Why was it created?

Until now, AI agents largely operated in silos. Let’s break it down.

AI agents are already everywhere. Whether it’s a virtual assistant in your CRM, a chatbot on your site, or an internal tool helping with analytics, these things are doing tasks for people. The problem? They’re not great at doing tasks with each other.

One vendor’s AI tool doesn’t know how to interact with another. So if your sales AI wants help from your customer support AI, there’s no common language or standard to make that handoff smooth.

A2A is Google’s answer to this. It’s an open protocol that lets AI agents discover each other, exchange capabilities, and delegate tasks, without needing to be built on the same platform or trained by the same team.

How Does A2A Work?

A2A follows a simple but powerful idea: make AI agents discoverable, describable, and cooperative.

Here’s what powers that:

1. Agent Cards

Every AI agent publishes a lightweight “card” (technically a JSON file) that describes:

What it can do
Where it lives (endpoint)
What kind of security/auth it requires
Optional metadata like documentation or developer contact

It’s a way for other agents to discover and understand this agent before initiating communication.

2. Task Lifecycle

Tasks sent between agents follow a clear structure:

They’re uniquely identified
They move through standard states: queued, in-progress, completed, failed, etc.
Agents can query each other for task status at any point

This ensures clarity in coordination, especially for longer or complex workflows.

3. Messaging and Artifacts

Agents can share more than just instructions. They can pass:

Structured messages (like summaries, prompts, commands)
Rich “artifacts” like files, documents, logs, or URLs

All of it is traceable and reusable, especially important for audit-heavy industries like finance or healthcare. Now, if you want to take the agentic AI workflow to the next level, you must know the intricacies of Agentic AI. Check out the Certified Agentic AI Developer™ program for a deeper dive.

4. Real-Time Updates

For long-running tasks, A2A supports streaming updates via Server-Sent Events (SSE) or notifications (webhooks). No one’s left guessing if a task is stuck.

Real Example of How Agent2Agent (A2A) Works

Here’s what this looks like in action:

A research assistant agent gets a prompt: “Write a summary of recent trends in generative finance models.”
It checks its own capabilities. Realizes it doesn’t have financial data access.
It looks up another agent in the org: the “Financial News Scraper Agent.” Finds its Agent Card. Looks good.
Sends a subtask: “Pull top 10 news pieces on GenAI in fintech.”
The scraper returns data. The assistant agent now calls the “Summarizer Agent.”
Once that’s done, a final “Writer Agent” formats the result and sends it off to a publishing queue.

This is not science fiction, it’s the kind of chained workflow A2A makes smooth, especially if these agents live in different environments.

What Problems Does A2A Actually Solve?

Here’s where A2A starts to shine, it’s not theoretical, and the use cases are immediately practical:

● Multi-agent business workflows

Imagine a customer support agent detecting a billing issue. Instead of opening a ticket, it contacts the finance agent directly, shares context, and gets a refund initiated, all automatically.

● Cross-platform AI integration

An HR team using Google Workspace might still rely on Workday for backend data. A2A makes it possible for their respective agents to sync tasks like interview scheduling, offer generation, and onboarding paperwork.

● Agent chaining for research or content

One agent gathers sources. Another summarizes. A third formats everything into a branded report or post. A2A is the thread tying them all together, even if they’re built in different environments.

Who’s Using A2A Already?

A2A isn’t just a Google thing. At launch, it was backed by over 50 companies including:

Salesforce
SAP
Box
ServiceNow
Intuit
LangChain
Workday

This early adoption matters. It means the protocol is more than theory, it’s being tested and embedded into real enterprise stacks.

Agent2Agent Protocol (A2A) vs Model Context Protocol (MCP)

A2A is often mentioned alongside Model Context Protocol (MCP), and the difference is important:

Feature	A2A	MCP
Purpose	Agent-to-agent collaboration	Agent-to-tool or model context
Focus	Delegation, messaging, status	Access to tools, models, data
Typical Use	Multi-agent task workflows	Enabling a model to use a tool/API

Together, A2A and MCP are building blocks for real, operational multi-agent systems, where agents talk to each other (A2A) and also interact with the outside world (MCP).

When Should Teams Start Using A2A?

It depends, but here’s a quick guide:

If you are…	A2A may be…
Building multiple AI agents	Immediately useful
Using tools from multiple vendors	A smart integration strategy
Managing workflows between teams	A time-saver and scaling asset
Running just a single-purpose bot	Maybe not needed (yet)

Even if implementation isn’t on the roadmap today, understanding A2A matters. It points to where enterprise AI is headed: modular, connected, agent-driven systems.

How to Get Started with A2A?

If implementation is on the radar, the best place to begin is the official A2A GitHub repo. It includes:

The full spec
Example agent cards
Sample server-side implementations
Security/auth guidelines
Community discussions

Most major AI agent frameworks (like LangGraph and CrewAI) are already building toward A2A compatibility.

Challenges of A2A

A2A is promising, but not perfect. Some limitations right now:

Standardization still evolving: You’ll still need to experiment to get things working.
Security and auth: OAuth + A2A is a good start, but cross-organization auth is tricky.
Monitoring and debugging: Tracing multi-agent workflows isn’t simple yet.
No native UX/UI yet: Agent-to-human interactions aren’t standardized, it’s still developer-first.

But this is early days. Google’s roadmap mentions future support for richer interaction modes (like voice/video) and more powerful UX integrations.

Conclusion

Google’s Agent2Agent protocol isn’t trying to reinvent the AI wheel, it’s fixing the spokes. By giving AI agents a shared language to talk, delegate, and collaborate, A2A unlocks a future where AI doesn’t just generate outputs, it gets things done, across teams, tools, and platforms.

This is one of the clearest examples of people-first AI infrastructure. Not just smart, but helpful.

If your team is working with multiple agents, or thinking about scale, A2A is worth paying attention to.

Google’s Agent2Agent Protocol (A2A)