Google’s Agent-to-Agent (A2A) protocol is becoming an important piece of infrastructure for the emerging agentic AI ecosystem.
Originally developed by Google, A2A was contributed to the Linux Foundation in June 2025, giving the project a neutral home and allowing companies across the technology industry to participate in its development.
Since then, the project has expanded rapidly.
By April 2026, more than 150 organizations were supporting A2A, with integrations across major cloud platforms and production deployments in industries including financial services, insurance, supply chain and IT operations.
The reason this matters is simple.
AI agents are becoming increasingly capable, but they cannot reach their full potential if every agent speaks a different technical language.
A2A is designed to solve that problem.
What Is Google’s A2A Protocol?
A2A stands for Agent-to-Agent.
It is an open protocol designed to allow AI agents built by different companies, vendors and frameworks to discover each other, communicate and coordinate tasks.
Instead of forcing every AI agent to be built inside the same ecosystem, A2A provides a common way for them to interact.
Think of it as a communication layer for AI agents.
One agent could specialize in travel.
Another could handle payments.
Another could manage customer support.
Another could work with enterprise data.
A2A is designed to allow those agents to work together even when they were developed by different companies.
Why Do AI Agents Need a Common Protocol?
Today’s AI ecosystem is highly fragmented.
Different companies build agents using different:
- Models
- Frameworks
- APIs
- Cloud platforms
- Tool systems
- Authentication methods
- Data sources
Without interoperability, companies often have to create custom connections between systems.
That becomes increasingly difficult as the number of agents grows.
A2A attempts to create a common communication layer so that agents can interact without requiring a custom integration for every possible combination.
Google Didn’t Keep A2A Under Its Own Control
One of the most significant aspects of the project is not the protocol itself.
It is where Google decided to put it.
In June 2025, Google contributed the A2A specification, SDKs and developer tooling to a new Agent2Agent project hosted by the Linux Foundation.
That means A2A is not simply a proprietary Google technology.
The project operates under a neutral open-governance model.
That makes it easier for competitors and other technology companies to participate without having to rely on Google’s control of the standard.
Why Neutral Governance Matters
Imagine if Google completely controlled the communication standard used by competing AI agents.
Other companies might hesitate to build their infrastructure around it.
A neutral organization changes the equation.
The Linux Foundation provides a governance structure where multiple companies can contribute to the technology.
This is particularly important for infrastructure standards.
Protocols tend to become more useful when competitors can agree on common rules.
The internet itself grew around open standards that were not controlled by a single company.
A2A is attempting something similar for AI agents.
Who Supports A2A?
A2A has attracted support from a growing group of technology companies.
The founding project included organizations such as:
- Amazon Web Services
- Cisco
- Microsoft
- Salesforce
- SAP
- ServiceNow
By April 2026, the Linux Foundation said the number of supporting organizations had grown to more than 150.
The ecosystem also includes companies such as IBM and other major enterprise technology providers.
That breadth is important because interoperability standards become more valuable as more independent systems adopt them.
A2A Has Moved Beyond an Experiment
A2A began as a relatively new idea in 2025.
Within its first year, however, the project had moved toward production use.
The Linux Foundation reported deployments across industries including:
- Supply chain
- Financial services
- Insurance
- IT operations
The project also released A2A 1.0, its first stable specification, with features aimed at production environments.
That is a significant transition.
The discussion is moving from:
“Could AI agents communicate?”
to:
“How should production AI agents communicate?”
What Can A2A Actually Do?
A2A is designed to provide a common mechanism for agents to:
- Discover other agents
- Understand their capabilities
- Exchange information
- Delegate tasks
- Coordinate work
- Track interactions
- Communicate across organizational boundaries
The important part is that the agents do not necessarily need to share the same underlying model or framework.
A Google-based agent could potentially communicate with an agent built by another company.
That is the fundamental idea behind interoperability.
A2A Is Not an AI Model
This is another important distinction.
A2A is not a chatbot.
It is not a large language model.
It does not generate answers by itself.
Instead, it provides communication infrastructure that AI agents can use.
A simple way to think about it is:
AI model = intelligence
AI agent = system that uses intelligence to perform tasks
A2A = communication layer that helps agents work together
That distinction makes the protocol easier to understand.
A2A vs MCP: What’s the Difference?
A2A and MCP are sometimes discussed together because both are important standards for agentic AI.
But they solve different problems.
A2A focuses on agent-to-agent communication.
MCP focuses on connecting agents or AI applications to tools and data.
For example, an AI agent might use MCP to connect to:
- A database
- A file system
- An API
- A software tool
A2A can then allow that agent to communicate with another AI agent.
The Linux Foundation describes the two protocols as complementary rather than competing standards.
A Simple Example of A2A
Imagine a business wants to arrange an international business trip.
Instead of one AI system doing everything, multiple specialized agents could work together.
A travel agent could:
Find flights and hotels.
A finance agent could:
Check the travel budget.
A calendar agent could:
Find suitable dates.
A compliance agent could:
Check company travel policies.
A2A could provide the communication layer allowing those agents to exchange information and coordinate their work.
The user would see one completed workflow.
Behind the scenes, multiple specialized agents could be collaborating.
Why Multi-Agent Systems Matter
One giant AI model does not necessarily need to do everything.
Specialized agents can be better suited to specific tasks.
For example:
- A coding agent can focus on software.
- A finance agent can focus on financial workflows.
- A research agent can focus on information gathering.
- A customer-support agent can handle customer interactions.
- A security agent can monitor threats.
The challenge is getting them to cooperate.
That’s where interoperability standards become important.
A2A Could Reduce Vendor Lock-In
One of the biggest potential benefits of open agent protocols is reducing dependence on a single AI provider.
Without standards, a company may build its entire agent infrastructure around one vendor.
Moving to another platform could then require rebuilding large portions of the system.
A common protocol can make agent ecosystems more portable.
Companies could potentially replace individual agents without replacing the entire architecture.
That is particularly valuable for large enterprises.
A2A and the “Internet of Agents”
The long-term vision goes beyond individual enterprise workflows.
If enough AI agents can communicate through common protocols, the internet could eventually contain an enormous network of specialized agents.
A user could interact with one personal AI agent.
That agent could communicate with:
- Shopping agents
- Banking agents
- Travel agents
- Research agents
- Business agents
- Software agents
- Government-service agents
This is sometimes described as an “Internet of Agents.”
Google has described A2A as part of a broader vision for collaborative agents operating across organizations and platforms.
Why This Could Change AI Applications
Today’s AI applications are often relatively self-contained.
You open an AI product.
You ask it a question.
It responds.
The agentic model is different.
The AI could potentially become an orchestrator that delegates parts of a task to specialized systems.
Instead of asking:
“Which AI app should I use?”
users may eventually ask:
“Can my AI agent get this done?”
The underlying agents could then decide which services they need.
A2A Could Become Infrastructure
If A2A becomes widely adopted, users may rarely notice that it exists.
That is often what happens with successful infrastructure.
People don’t think about HTTP every time they open a website.
They don’t think about DNS when entering a domain name.
Similarly, users may not think about A2A when several AI agents collaborate.
They would simply experience an AI system completing a complicated task.
Security Becomes More Important
Interoperability also creates security challenges.
When one agent communicates with another, the systems need to establish:
- Who the agent is
- What it is allowed to do
- What information it can access
- Whether messages can be trusted
- How actions are authorized
- How activity is monitored
A network of autonomous agents could become powerful.
It could also become a new attack surface.
This is why A2A’s future development will need to involve security, identity and authorization standards.
Google Has Recognized the Need for Additional Standards
When Google contributed A2A to the Linux Foundation, it also pointed toward additional areas that need development.
These include:
- Agent identity
- Delegated authority
- Governance
- Security
- Reputation
- Trust
Those issues become increasingly important as agents begin interacting across organizational boundaries.
A2A Is Part of a Larger Standards Movement
A2A is not operating alone.
The AI industry is increasingly creating open projects designed to solve different pieces of the agentic AI infrastructure problem.
The Linux Foundation has become an important home for these efforts.
Projects such as AGNTCY focus on agent discovery, identity, messaging and observability.
Meanwhile, the Agentic AI Foundation is working on open standards and infrastructure for interoperable agent systems.
This suggests that the industry increasingly sees interoperability as infrastructure rather than an optional feature.
Why Big Tech Companies Are Supporting Open Standards
There is an interesting strategic reason.
Even companies competing directly in AI can benefit from interoperability.
If customers are afraid of vendor lock-in, they may delay adopting agent technology.
Open standards can reduce that concern.
A company may feel more comfortable deploying AI agents if it knows those systems can communicate with other platforms.
That can expand the overall market.
In other words, competitors can benefit from agreeing on the basic infrastructure while continuing to compete on models and products.
The Agentic AI Race Is Moving Beyond Models
The AI race was initially dominated by model performance.
Companies competed over:
- Benchmark scores
- Context windows
- Reasoning
- Coding
- Multimodal capabilities
- Inference costs
The agentic era introduces another competitive layer.
Now companies also need:
- Agent frameworks
- Tool systems
- Memory
- Identity
- Security
- Interoperability
- Communication protocols
A2A sits directly inside this new infrastructure layer.
What Does A2A Mean for Developers?
For developers, standards like A2A could reduce the amount of custom integration work required.
Instead of building one-off communication systems for every agent, developers can potentially build around a common protocol.
That could make it easier to create applications containing multiple specialized agents.
It could also make it easier to replace one agent with another.
What Does A2A Mean for Businesses?
For businesses, the biggest benefit could be flexibility.
Companies may eventually be able to combine AI services from different vendors.
For example, one agent could handle customer support while another handles financial analysis.
The company would not necessarily need both systems to come from the same provider.
That creates a more competitive AI market.
Could A2A Become the Standard for AI Agents?
It is too early to say.
A2A has strong industry support and growing production adoption, but standards battles can take years.
Other protocols and approaches will continue to develop.
The eventual ecosystem could contain multiple complementary standards rather than one protocol controlling everything.
Still, A2A’s rapid adoption gives it a strong position.
The Biggest Challenge Is Trust
Communication is only useful if agents can trust the systems they interact with.
Imagine an AI agent receiving a request from another agent.
How does it know:
- Who sent it?
- Whether the request is legitimate?
- Whether the requested action is authorized?
- Whether the information is accurate?
- Whether the other agent has been compromised?
These questions become increasingly important as agents begin making real-world decisions.
A2A Could Enable More Autonomous Businesses
The long-term implications go beyond chatbots.
Businesses could eventually operate networks of AI agents that coordinate:
- Sales
- Customer service
- Procurement
- Finance
- Logistics
- Software development
- Research
- Compliance
Instead of one AI doing everything, specialized agents could operate as digital teams.
A2A could provide part of the communication infrastructure required to make that possible.
The Bigger Picture
Google’s decision to move A2A into the Linux Foundation ecosystem is significant because it changes the protocol from a Google-originated technology into a broader industry standard.
The project now has a neutral governance structure and support from major technology companies.
Its rapid adoption suggests that companies are preparing for a world where AI agents will not operate independently.
They will work together.
The future of agentic AI may therefore depend not only on how intelligent individual models become, but also on how effectively those models can communicate, delegate and collaborate.
Bottom Line
Google’s A2A protocol is becoming a major open standard for AI-agent interoperability.
Originally created by Google, A2A was contributed to the Linux Foundation in 2025 to provide neutral governance and encourage broader industry participation.
By 2026, the project had grown to more than 150 supporting organizations and was seeing production adoption across multiple industries.
A2A does not compete directly with AI models.
Instead, it provides a communication layer that allows different AI agents to discover one another, exchange information and coordinate tasks.
Together with technologies such as MCP and AGNTCY, it could become part of the infrastructure behind a future multi-agent internet.
The biggest question now is not whether AI agents will communicate.
It is how large and interconnected that agent ecosystem will become.
FAQ
What is Google’s A2A protocol?
A2A, or Agent-to-Agent, is an open protocol designed to allow AI agents from different vendors and frameworks to discover, communicate and coordinate with one another.
Why did Google move A2A to the Linux Foundation?
Google contributed A2A to the Linux Foundation to establish neutral governance and encourage broader industry participation. This helps prevent the protocol from being controlled by a single vendor.
What does A2A stand for?
A2A stands for Agent-to-Agent.
Is A2A an AI model?
No. A2A is a communication protocol, not an AI model. It provides infrastructure that AI agents can use to communicate and collaborate.
What is the difference between A2A and MCP?
A2A focuses primarily on communication between AI agents, while MCP provides a standardized way for AI applications and agents to connect with tools and data. The two can work together.
Which companies support A2A?
The A2A ecosystem includes major companies such as Google, AWS, Cisco, Microsoft, Salesforce, SAP and ServiceNow, with the Linux Foundation reporting more than 150 supporting organizations by April 2026.
Can AI agents from different companies communicate using A2A?
That is one of A2A’s main purposes. The protocol is designed to provide a common communication framework so agents built by different vendors and frameworks can collaborate.
Why is AI-agent interoperability important?
Without interoperability, businesses may need custom integrations between every AI system they use. A common protocol can reduce that complexity and make multi-agent systems easier to build and scale.
Could A2A become the standard for AI agents?
It is possible, but it is too early to know. A2A has significant industry support and production adoption, but the agentic AI standards ecosystem is still developing.
What is the “Internet of Agents”?
The Internet of Agents refers to a future in which large numbers of AI agents can discover and communicate with each other across different organizations and platforms, allowing them to collaborate on complex tasks.
