A2A (Agent2Agent) is an open protocol designed to help AI agents communicate, delegate tasks, exchange information, and collaborate across different applications, frameworks, and vendors.

As AI systems become more capable, businesses are increasingly moving from single-purpose chatbots toward agentic systems. One agent might handle customer support, another might analyze financial information, and another might manage logistics.

That creates an important architectural question:

How can independently built AI agents work together without requiring a custom integration for every possible pair?

A2A is designed to address that problem.

Originally introduced by Google in April 2025, A2A was later donated to the Linux Foundation ecosystem. In March 2026, the project released A2A v1.0, its first stable production-ready specification. In August 2026, A2A was accepted as a Growth Stage project at the Agentic AI Foundation (AAIF).

This guide explains what A2A is, how it works, what Agent Cards and Tasks do, how A2A compares with MCP, where it can be useful, and where it may simply add unnecessary complexity.

What Is the A2A Protocol?

A2A stands for Agent2Agent.

It is an open standard for communication and interoperability between independent AI agent systems.

The key word is independent.

Two agents communicating through A2A do not necessarily need to:

Use the same AI model
Use the same programming language
Use the same framework
Come from the same company
Expose their internal memory
Expose their internal tools
Share the same implementation

Instead, A2A provides a common interaction model through which one agent can communicate with another.

The official A2A specification describes the protocol as a way for independent, potentially opaque agent systems to discover capabilities, negotiate interaction modalities, manage collaborative tasks, and exchange information without requiring access to each other’s internal state, memory, or tools.

A simple example

Imagine an AI customer-service system.

The main support agent receives:

“Where is my order, and am I eligible for a refund?”

Rather than performing every operation itself, it could delegate work to specialized agents:

                    Customer
                       │
                       ▼
                Support Agent
                  /         \
                A2A         A2A
                /             \
               ▼               ▼
       Logistics Agent    Payment Agent
               │               │
               ▼               ▼
        Shipping System    Payment System

The customer still experiences one conversation.

Behind the scenes, specialized agents perform different parts of the job.

That is the basic idea behind agent-to-agent interoperability.

Why Was A2A Created?

Traditional APIs already allow software systems to communicate.

So why introduce another protocol?

The problem becomes more complicated when the number of independent AI agents grows.

Imagine a business with:

A sales agent
A finance agent
A research agent
A support agent
A logistics agent
A compliance agent

If every agent has a proprietary communication interface, developers may need custom integrations between many different systems.

Each integration can introduce its own:

Authentication mechanism
Data structures
Error handling
Documentation
Deployment requirements
Monitoring
Maintenance

A common protocol can reduce the amount of agent-specific integration logic.

This was one of the motivations behind Google’s original A2A announcement in April 2025. Google described A2A as a protocol intended to allow agents built by different vendors and using different frameworks to collaborate across organizational and technological boundaries.

The project was subsequently moved into the Linux Foundation ecosystem, giving it a more neutral governance structure.

How Does A2A Work?

A2A can be understood through two primary roles.

1. A2A Client

The A2A client is the application, service, or agent that initiates communication.

It might ask another agent to:

Analyze a document
Research a topic
Calculate a result
Check inventory
Create a report
Perform a business task

The client does not necessarily need to know how the remote agent performs the work.

2. A2A Server

The A2A server exposes an AI agent or agentic system through an A2A-compatible interface.

From the client’s perspective, the remote agent can remain largely opaque.

The client needs to understand what the agent can do and how to communicate with it—not necessarily how the agent reasons internally or which tools it uses.

A simplified architecture looks like this:

User
 │
 ▼
Client Agent
 │
 │ A2A
 ▼
Remote Agent
 │
 ├── Internal Model
 ├── Internal Tools
 ├── Internal Data
 └── Internal Workflow

The important boundary is that the remote agent can expose a useful capability without exposing its internal implementation.

Agent Cards: How Agents Describe Their Capabilities

One of A2A’s most important concepts is the Agent Card.

An Agent Card is a structured JSON document that describes an agent and tells potential clients how to interact with it.

You can think of it as a combination of:

Digital business card
Capability profile
Service description
Integration metadata

An Agent Card can describe information such as:

Agent identity
Description
Service endpoint
Supported protocol interfaces
Capabilities
Skills
Supported input and output modalities
Authentication requirements
Extensions

The official documentation describes Agent Cards as a discovery mechanism that allows clients to determine whether an agent is appropriate for a particular task and understand how to interact with it.

Example concept

Imagine a company operates a financial-analysis agent.

Its capabilities might be described conceptually as:

Financial Analysis Agent

Skills:
- Financial report analysis
- Revenue analysis
- Risk assessment
- Forecasting

Input:
- Text
- Files
- Structured data

Output:
- Text
- Structured data
- Reports

Authentication:
- OAuth 2.0

Another compatible agent can use this information to determine whether the financial agent is suitable for a particular job.

Agent Discovery

Agent Cards become especially useful when an ecosystem contains many independent agents.

Suppose an AI system needs an agent capable of:

“Analyze European VAT compliance documents.”

Instead of hard-coding every possible specialist, an agent ecosystem can use discovery mechanisms to identify available agents and inspect their advertised capabilities.

This doesn’t mean that A2A automatically creates a global marketplace of trustworthy agents.

Discovery still requires appropriate infrastructure, trust models, authentication, authorization, and governance.

But the protocol provides standardized information that makes agent discovery possible.

A2A Tasks: The Unit of Work

A2A is not simply a protocol for sending chat messages between bots.

One of its central concepts is the Task.

A Task represents a stateful unit of work.

For example:

“Analyze this 500-page financial report and produce a risk summary.”

That work might not finish immediately.

A task can move through different states during its lifecycle.

A simplified example:

Submitted
    │
    ▼
Working
    │
    ▼
Input Required
    │
    ▼
Working
    │
    ▼
Completed

A task could also fail or be canceled depending on the workflow.

This model is useful because many real-world agent operations are not simple request-and-response interactions.

Examples include:

Processing large documents
Generating reports
Research workflows
Compliance analysis
Complex business automation
Data processing

The current A2A documentation defines Tasks as stateful units of work with a lifecycle and unique identifiers.

Messages, Parts, and Artifacts

A2A separates communication from the actual deliverables produced during a task.

Messages

A Message represents a turn of communication between a client and an agent.

For example:

“Analyze this company’s financial performance.”

The response could contain another message explaining what the agent is doing or asking for additional information.

Parts

Messages and artifacts contain Parts.

A Part can carry different types of content, including:

Text
File data
URLs
Structured data

This allows A2A interactions to go beyond plain natural-language text.

Artifacts

An Artifact is a concrete output generated during a task.

For example:

Task
│
├── Messages
│
└── Artifacts
    ├── Financial Report.pdf
    ├── Risk Analysis.json
    └── Forecast.xlsx

An artifact could represent a document, image, spreadsheet, structured result, or another tangible output.

This distinction is important because production AI systems often need to exchange actual data and files—not just conversational responses.

What Can A2A Agents Exchange?

A2A is designed to support multiple content types.

Depending on the implementation, agents can exchange information such as:

Text
Files
URLs
Structured JSON data
Binary data
Metadata

For example, one agent might return:

{
  "customer_score": 87,
  "risk_level": "low",
  "approved": true
}

Another agent can consume that structured information without having to parse a natural-language paragraph.

This is particularly useful for automated workflows where the output of one agent becomes the input to another.

A2A Is More Than AI Agents “Chatting”

A common mistake is to imagine A2A as two chatbots having a conversation.

The more useful mental model is:

A2A enables agents to delegate and collaborate on work.

Consider an investment research system.

A main research agent might delegate:

                    Research Agent
                    /      |       \
                  A2A     A2A      A2A
                  /        |         \
                 ▼         ▼          ▼
           Market Agent  Finance   News Agent
                          Agent

The specialized agents might produce:

Market information
Financial analysis
News summaries

The main agent can then combine those outputs into a final research product.

The advantage isn’t that the agents “talk.”

The advantage is that different agents can specialize in different responsibilities.

A2A vs MCP: What Is the Difference?

This is one of the most important distinctions for anyone learning about modern AI infrastructure.

A simple rule is:

MCP connects AI applications and agents with tools and data.

A2A connects AI agents with other agents.

The two protocols therefore solve different problems.

MCP

Think:

Agent → Tools/Data

Examples can include:

Databases
Files
Search systems
Business applications
Internal services

A2A

Think:

Agent → Agent

Examples include:

Research Agent
Finance Agent
Legal Agent
Support Agent
Logistics Agent

A2A and MCP can also appear in the same architecture.

                         Main Agent
                        /          \
                      A2A          A2A
                      /              \
                     ▼                ▼
              Research Agent     Finance Agent
                     │                │
                    MCP              MCP
                     │                │
                     ▼                ▼
                Search/Data      Business Tools

The official A2A project itself describes MCP as a way for agents to access tools and data while A2A provides communication between agents.

The easiest way to remember it

MCP equips the agent.

A2A connects the agent.

A2A vs Traditional APIs

A2A does not make traditional APIs obsolete.

APIs remain fundamental to software architecture.

The difference is that A2A defines an interaction model specifically designed for agent interoperability.

A conventional API might expose:

POST /analyze

The caller needs to know:

The endpoint
The request schema
Authentication
Expected response
Error behavior

A2A introduces additional concepts relevant to agents, including:

Agent Cards
Skills
Tasks
Messages
Parts
Artifacts
Capability negotiation
Streaming
Push notifications
Agent discovery

The protocol therefore operates at a different abstraction level than a simple application endpoint.

A2A can also be implemented using different protocol bindings. The current documentation lists JSON-RPC, gRPC, and HTTP+JSON/REST as standard bindings.

A2A vs Multi-Agent Frameworks

A2A is also different from a multi-agent framework.

A framework helps developers build and orchestrate agents.

A protocol helps independently built systems communicate with each other.

Think of the distinction like this:

Multi-Agent Framework
        │
        ▼
Builds / Orchestrates Agents
        │
        ▼
       A2A
        │
        ▼
Communicates With Other Agents

You can therefore build an agent using a particular framework and expose that agent through A2A.

The remote agent does not necessarily need to use the same framework.

That is where interoperability becomes valuable.

Real-World Example: AI Customer Support

Consider an online retailer receiving:

“Where is my order, and can I get a refund?”

A possible multi-agent architecture could look like this:

                         Customer
                            │
                            ▼
                    Support Agent
                       /       \
                     A2A       A2A
                     /           \
                    ▼             ▼
           Logistics Agent   Payment Agent
                    │             │
                   MCP           MCP
                    │             │
                    ▼             ▼
             Shipping Data    Payment Data

Step 1: Support Agent

The support agent interprets the customer’s request.

It determines that it needs two types of information.

Step 2: Logistics Agent

It delegates the shipping question:

“What is the current status of order #12345?”

The logistics agent retrieves the relevant information.

Step 3: Payment Agent

The support agent delegates the refund question:

“Does this order qualify for a refund under the applicable policy?”

The payment or financial agent evaluates the request.

Step 4: Final Response

The support agent combines the results and provides the customer with one answer.

The customer doesn’t need to know how many agents participated.

This illustrates the practical distinction:

A2A handles agent-to-agent collaboration.

MCP can help individual agents access their underlying tools and data.

Why Businesses Should Care About A2A

The strongest argument for A2A is not simply “AI agents can communicate.”

It is specialization without complete isolation.

A company can potentially create different agents for different business functions.

Sales Agent

Handles sales workflows and customer qualification.

Finance Agent

Handles financial analysis and related business processes.

Logistics Agent

Works with inventory, shipping, and fulfillment information.

Support Agent

Handles customer-facing interactions.

Compliance Agent

Reviews workflows against applicable policies and requirements.

Instead of one enormous AI system attempting to understand every business process, specialized systems can collaborate where necessary.

1. Interoperability

A2A is particularly relevant when agents are developed by different teams or vendors.

The official project explicitly targets interoperability across different frameworks, languages, and vendors.

That can reduce dependence on a single implementation.

2. Specialization

A narrow agent can focus on a specific responsibility.

This can make system boundaries clearer and allow teams to manage capabilities independently.

However, specialization should be intentional.

Adding an agent merely because the architecture is “multi-agent” does not automatically improve the system.

3. Long-Running Workflows

Some business operations require more than an immediate response.

Examples include:

Large document analysis
Research
Compliance workflows
Report generation
Data processing
Enterprise automation

A2A provides mechanisms for tracking tasks and receiving updates during longer-running operations.

4. Cross-Organization Collaboration

One of the more ambitious possibilities is collaboration between agents controlled by different organizations.

For example:

Retailer Agent
      │
      ▼
Supplier Agent
      │
      ▼
Shipping Agent
      │
      ▼
Payment Agent

Each organization could maintain control over its own agent and internal systems while exposing a standardized interface for specific interactions.

This does not eliminate trust, legal, security, or governance problems.

It simply provides a technical foundation for interoperability.

How A2A Handles Long-Running Tasks

A2A supports several interaction patterns.

Request and Response / Polling

A client can send a request and receive a response.

For longer-running work, the client can retrieve task status and results as the task progresses.

This is useful when the operation does not require a continuously open connection.

Streaming

For supported agents, streaming allows clients to receive incremental task updates.

For example:

Task Started
     ↓
Analyzing Document
     ↓
Extracting Data
     ↓
Generating Report
     ↓
Completed

This can make long-running workflows more observable.

Push Notifications

For asynchronous workflows, an agent can send updates through a configured notification mechanism.

This can be useful when:

A task takes a long time
The client doesn’t want to maintain a continuous connection
The result may arrive significantly later

The official A2A documentation lists polling, streaming, and push notifications among its interaction mechanisms.

A2A in 2026: What Has Changed?

A2A is no longer simply an experimental proposal.

Several developments are important for understanding its position in 2026.

A2A 1.0

In March 2026, the project released A2A v1.0, the first stable production-ready version of the protocol.

The v1.0 specification introduced structural changes and clarified areas including Agent Cards, protocol interfaces, streaming events, and OAuth security.

For example, v1.0 uses supportedInterfaces in the Agent Card to describe the interfaces an agent exposes, rather than relying on the older v0.x structure. It also modernized OAuth support, including PKCE-related security improvements.

This matters because older A2A tutorials may describe v0.x fields or APIs that no longer represent the current specification.

A2A and the Agentic AI Foundation

On August 27, 2026, the A2A project announced that it had been accepted as a Growth Stage project within the Agentic AI Foundation (AAIF).

The project describes A2A as a horizontal orchestration layer for communication and collaboration between agents.

For developers, this is a more meaningful signal than simply seeing another protocol announcement: governance and interoperability matter when a protocol is intended to connect systems from multiple organizations.

What Could A2A Enable?

A2A does not guarantee that all of these scenarios will become mainstream, but the protocol makes several architectures easier to imagine.

Agent Discovery

An AI system could identify specialized agents based on their declared capabilities.

For example:

“Find an agent capable of analyzing VAT compliance documents.”

The Agent Card provides standardized metadata that can support this kind of discovery.

Cross-Company Automation

Imagine:

Company A Agent
      │
      ▼
Company B Agent
      │
      ▼
Company C Agent

Instead of building a completely proprietary integration for every interaction, companies could potentially expose selected capabilities through a common protocol.

Security and authorization would remain essential.

AI Research Networks

A research system could delegate work to specialized agents:

Literature research
Data analysis
Market research
Fact checking
Report generation

The main agent could then combine the outputs.

Agentic Commerce

A shopping workflow could eventually involve several specialized agents:

Shopping Agent
     │
     ├── Product Research Agent
     ├── Price Agent
     ├── Inventory Agent
     └── Payment Agent

A2A could be one component in such an architecture.

It would not, by itself, solve payment security, identity, commerce rules, fraud prevention, or transaction authorization.

How Developers Can Start With A2A

You don’t need dozens of agents to experiment with the protocol.

A small project is often a better starting point.

Step 1: Define One Specialized Agent

Choose a narrow capability.

For example:

Invoice Analysis Agent

Its responsibility:

Analyze invoices and extract useful financial information.

Keep the scope small.

Step 2: Define Its Skills and Inputs

Document:

What the agent can do
What inputs it accepts
What outputs it produces
Which modalities it supports
What authentication is required
Which operations may take longer to complete

This information becomes part of the agent’s public contract.

Step 3: Create an Agent Card

The Agent Card tells clients what the agent is and how to communicate with it.

The current v1.0 specification uses supportedInterfaces to describe protocol interfaces and versions.

Step 4: Implement the A2A Interface

Use the official specification and SDK resources rather than copying an old tutorial that may target an earlier protocol version.

The current A2A documentation provides implementation resources and SDKs for multiple programming languages.

Step 5: Add a Second Agent

For example:

Accounting Agent

The Accounting Agent could delegate invoice analysis to the Invoice Agent.

Accounting Agent
       │
       │ A2A
       ▼
Invoice Agent
       │
       │
      MCP
       │
       ▼
Business Data / Tools

This is a useful example of how A2A and MCP can complement each other rather than compete.

When Should You Actually Use A2A?

This is probably the most practical question.

Don’t use A2A simply because it is becoming popular.

Use it when you have a real interoperability or agent-collaboration problem.

A2A makes sense when:

Multiple independent agents need to collaborate
Agents are built by different teams
Agents use different frameworks
Agents may come from different vendors
You need task delegation between agents
Some operations are long-running
You expect the agent ecosystem to grow
You need a standardized boundary between agent systems

A2A may be unnecessary when:

You have one agent
You have a few simple tools
Everything is inside one application
There is no agent-to-agent delegation
A normal internal API already solves the problem

A useful engineering rule is:

If a simple API solves the problem, don’t add A2A just for the sake of using A2A.

Protocols add value when standardization solves a genuine problem.

5 Common A2A Mistakes

Mistake #1: Thinking A2A Replaces MCP

It doesn’t.

The simplest distinction is:

MCP → agent-to-tools/data

A2A → agent-to-agent

They can be used together.

Mistake #2: Creating Too Many Agents

More agents do not automatically mean a better architecture.

Every additional agent can introduce:

Network calls
Latency
Failure points
Authentication requirements
Monitoring requirements
Debugging complexity
Infrastructure costs

Use specialization where it creates a clear benefit.

Mistake #3: Ignoring Security

A2A does not automatically secure an application.

The current A2A enterprise guidance emphasizes standard web security mechanisms, including TLS, OAuth 2.0, OpenID Connect, authentication, authorization, monitoring, and least-privilege access.

Developers should carefully consider:

Who can invoke an agent
Which skills each caller can access
What data the agent can retrieve
Which actions the agent can perform
How credentials are handled
How interactions are logged
How agents are monitored

Authentication and authorization are separate concerns.

A caller being authenticated does not automatically mean it should have permission to perform every available operation.

Mistake #4: Treating Everything as Text

AI systems increasingly work with structured information and files.

A2A supports Parts containing text, files, URLs, and structured data, while Artifacts can represent concrete outputs generated during a task.

Design workflows around the actual data being exchanged rather than converting everything into natural language unnecessarily.

Mistake #5: Assuming Open Protocol Means No Vendor Lock-In

An open protocol can improve interoperability, but it does not magically eliminate vendor dependence.

A system may still depend on:

Proprietary models
Proprietary databases
Proprietary tools
Cloud infrastructure
Vendor-specific extensions

A2A can reduce one form of integration lock-in, but overall architecture still matters.

A2A Security: What Developers Should Know

Security deserves special attention because agent-to-agent communication can cross organizational and trust boundaries.

A2A’s current enterprise guidance separates authentication from authorization.

Authentication answers:

Who is making this request?

Authorization answers:

What is that caller allowed to do?

The protocol’s security guidance recommends using established web mechanisms rather than embedding identity directly into normal A2A payloads. Authentication requirements are advertised by the Agent Card, while credentials are generally handled at the HTTP/transport layer.

A production implementation should also consider:

Least privilege

Give an agent only the access it actually needs.

Agent identity

Know which application or agent is making the request.

Skill-level authorization

Not every authenticated caller should necessarily be allowed to invoke every skill.

Data protection

Avoid exposing sensitive information to agents that don’t require it.

Observability

Log and monitor important agent interactions.

Extension security

Treat extension-provided data and behavior as untrusted until properly validated. The A2A documentation specifically warns that extensions must not bypass existing authentication and authorization controls.

The Biggest Advantage of A2A

If you remember only one concept from this article, remember this:

An agent should be able to collaborate with another agent without needing to know how that agent was built internally.

A sales agent does not need to know whether a financial agent uses Python, Java, Go, or another language.

It does not need to know which model powers it.

It does not need access to its private memory.

It needs to know:

What the agent can do
How to communicate with it
What information it accepts
What it can return
What authentication is required

That separation is the heart of interoperability.

A2A’s Limitations

A2A is useful, but it is not a magic solution for every AI architecture.

Complexity

A multi-agent system can be considerably harder to debug than a single-agent application.

When something goes wrong, the failure could originate from:

The client
The remote agent
Authentication
Authorization
Networking
Task management
External tools
Model behavior
Incorrect delegation

More components mean more possible failure points.

Latency

Consider:

Agent A
   ↓
Agent B
   ↓
Agent C
   ↓
Agent D

Every additional network interaction can introduce latency.

A carefully designed single-agent workflow may therefore outperform an unnecessarily complex multi-agent architecture.

Reliability

Agent systems can produce incorrect results.

Adding additional agents can create additional opportunities for:

Incorrect delegation
Conflicting outputs
Failed requests
Invalid assumptions
Partial failures

The important question isn’t simply:

“Can the agents communicate?”

It is:

“Can the overall workflow communicate reliably enough for the business requirement?”

Governance

Organizations also need clear answers to questions such as:

Which agents are trusted?
Who owns each agent?
Who can invoke it?
Which data can it access?
Which actions can it perform?
How are interactions audited?
How are protocol changes managed?

These questions become increasingly important as agent ecosystems grow.

A2A vs MCP vs APIs: Quick Comparison

Technology	Primary purpose	Typical relationship
A2A	Agent interoperability	Agent ↔ Agent
MCP	Access to tools and data	Agent ↔ Tool/Data
Traditional API	Software/service integration	Application ↔ Service
Multi-agent framework	Building/orchestrating agents	Developer → Agent System

These technologies are not necessarily alternatives.

A production architecture could use all four.

For example:

                    Main Agent
                   /          \
                 A2A          A2A
                 /              \
                ▼                ▼
        Research Agent      Finance Agent
                │                │
               MCP              MCP
                │                │
                ▼                ▼
          Search / DB        Business APIs

The framework builds the agents.

A2A connects the agents.

MCP connects agents to tools and data.

APIs can connect the underlying software systems.

Frequently Asked Questions

What does A2A stand for?

A2A stands for Agent2Agent.

It is an open protocol designed to support communication and interoperability between AI agents.

Is A2A made by Google?

A2A was originally introduced by Google in April 2025.

The project was later donated to the Linux Foundation ecosystem and is now developed as an open project with broader industry participation.

What is the latest A2A version?

As of September 2026, the current released specification is A2A v1.0.0. The official documentation identifies 1.0.0 as the latest released version.

Is A2A the same as MCP?

No.

MCP focuses on connecting AI applications and agents with tools and data.

A2A focuses on communication between independent agents.

They can be complementary.

Does A2A require the same AI model?

No.

A2A is designed for interoperability between independently built agent systems, including systems using different frameworks, languages, and vendors.

Does A2A expose an agent’s internal memory?

Not necessarily.

A remote agent can operate as an opaque system. Its internal memory, tools, and implementation do not need to be exposed to the client.

Can A2A exchange files?

Yes.

A2A Parts can carry text, file data, URLs, and structured data. Artifacts can represent generated documents, images, structured results, and other outputs.

Can A2A handle long-running tasks?

Yes.

Tasks are stateful units of work, and A2A supports mechanisms including polling, streaming, and push notifications for different workflow requirements.

Is A2A useful for small AI projects?

Sometimes, but it may be unnecessary.

If you have one agent and a few internal tools, an ordinary API or MCP integration may be simpler.

A2A becomes more interesting when independent agents need to communicate or delegate work.

Final Verdict: Is A2A Important in 2026?

Yes—but its importance should be understood correctly.

A2A is not valuable simply because “AI agents are the future.”

Its value comes from solving a concrete engineering problem:

How can independently built AI agents communicate and collaborate without requiring every integration to be custom-built?

That problem becomes more significant as organizations build larger agent ecosystems.

A future architecture might look like:

                           USER
                             │
                             ▼
                       Main Agent
                      /          \
                    A2A          A2A
                    /              \
                   ▼                ▼
           Research Agent      Finance Agent
                  │                  │
                 MCP                MCP
                  │                  │
                  ▼                  ▼
             Search/Data       Business Tools
                  │                  │
                  └────────┬─────────┘
                           ▼
                     Final Result
                           │
                           ▼
                          USER

The key distinction is simple:

MCP helps an agent work with tools and data.

A2A helps agents work with other agents.

A2A v1.0 and its continued development in 2026 make the protocol more significant than it was when Google first announced it in 2025. The project’s move into the Agentic AI Foundation also reinforces its focus on open, interoperable agent infrastructure.

But A2A is not something every application needs.

The best architecture is still the simplest one that reliably solves the problem.

If one agent and a normal API are enough, use them.

If multiple independent agents need to discover capabilities, delegate tasks, exchange structured information, and collaborate across technology boundaries, A2A becomes much more compelling.