Milind Daraniya

What Is A2A Protocol? How AI Agents Communicate With Each Other

Published August 1st, 2026 22 min read

AI agents are becoming more powerful every month.

Earlier, we mainly used AI as a chatbot. We asked a question, the AI generated an answer, and the conversation ended there.

Now the situation is changing.

AI agents can use tools, access APIs, read data, execute tasks, work with other systems, and complete multiple steps without requiring a developer to manually control every action.

But there is one important problem.

What happens when one AI agent needs another AI agent to complete a task?

For example, imagine that you have an AI sales agent, an inventory agent, an accounting agent, and a customer-support agent.

A customer asks:

"Can you check whether this product is available, create an order, generate an invoice, and send the invoice to the customer?"

This is not necessarily one small AI task.

Different agents may be responsible for different parts of the process.

This is where A2A, or Agent2Agent Protocol, becomes important.

A2A is an open protocol designed to allow independent AI agents to discover each other, communicate, delegate tasks, exchange information, and collaborate.

The current A2A specification describes a standard way for agents built with different frameworks, languages, or vendors to communicate without exposing their internal implementation.

What Is A2A Protocol?

A2A stands for Agent2Agent.

It is an open standard for communication between independent AI agents.

The basic idea is simple:

AI Agent A
     |
     | A2A
     |
     v
AI Agent B

Agent A does not need to know how Agent B works internally.

It does not need to know:

  • Which LLM Agent B uses
  • Which programming language it uses
  • Which database it uses
  • Which framework it uses
  • How its internal memory works
  • Which tools it has internally

Instead, Agent B exposes its capabilities through the A2A protocol.

This is important because the future of AI applications will probably not be based on one large agent doing everything.

We may have many specialized agents working together.

For example:

                    User
                      |
                      v
                Main AI Agent
                 /    |     \
                /     |      \
               v      v       v
          Sales Agent  Inventory Agent  Finance Agent
               |           |               |
               v           v               v
             CRM        Database         Accounting

The main agent can delegate different jobs to specialized agents.

That is the main problem A2A is trying to solve.

Why Do We Need A2A?

Let's take a practical example.

Suppose I am building a SaaS application with Laravel.

The application has:

  • CRM
  • Inventory
  • Booking
  • Invoice
  • Accounting
  • Customer support

Now I want to add AI.

Instead of creating one huge AI agent with access to everything, I can create specialized agents.

For example:

CRM Agent
Inventory Agent
Booking Agent
Invoice Agent
Support Agent

A user might ask:

"Find the customer's previous bookings, check the inventory, create an invoice and send it to the customer."

The request can be divided into multiple tasks.

User
 |
 v
AI Coordinator Agent
 |
 +----> CRM Agent
 |
 +----> Inventory Agent
 |
 +----> Invoice Agent
 |
 +----> Email Agent

The coordinator does not need to implement every business function itself.

It can communicate with other agents.

This makes large AI systems easier to divide into smaller services.

A2A vs Normal API

You may be thinking:

"We already have REST APIs. Why do we need A2A?"

This is a very good question.

A normal API usually exposes predefined endpoints.

For example:

GET /api/products/123
POST /api/orders
POST /api/invoices

The developer knows the API structure and explicitly writes code to call those endpoints.

With AI agents, the situation can be different.

An agent may need to:

  1. Discover another agent.
  2. Understand what that agent can do.
  3. Send a task.
  4. Wait for the task to complete.
  5. Receive status updates.
  6. Provide additional information.
  7. Receive the final result.

A2A provides concepts specifically for this type of agent collaboration.

The protocol includes concepts such as Agent Cards, Messages, Tasks, Parts, Artifacts, context IDs, streaming and push notifications.

What Is an A2A Agent?

An A2A system normally involves two important sides.

A2A Client

The client initiates communication.

It can be:

  • An AI agent
  • An application
  • Another service
  • An automated system

A2A Server

The server represents the remote agent.

It exposes an A2A-compatible endpoint and processes tasks.

For example:

Client Agent
     |
     | A2A request
     v
Remote Agent
     |
     v
Process Task
     |
     v
Return Result

The remote agent can remain a black box.

The client does not need access to the remote agent's internal memory or tools.

What Is an Agent Card?

One of the most interesting concepts in A2A is the Agent Card.

Think about an Agent Card like a digital business card for an AI agent.

Suppose I have an Inventory Agent.

Its Agent Card can describe things like:

Agent Name:
Inventory Agent

Description:
Handles inventory and stock-related operations.

Skills:
- Check stock
- Search products
- Check warehouse availability
- Reserve inventory

Endpoint:
https://inventory.example.com/a2a

Authentication:
OAuth

Another agent can use this information to understand what the Inventory Agent is capable of doing.

The official A2A documentation describes the Agent Card as metadata containing information such as the agent's identity, capabilities, endpoint, skills, and authentication requirements.

This is important for agent discovery.

Instead of hardcoding every possible agent into an application, an agent can discover another agent's capabilities.

What Is an A2A Task?

A2A is not only about sending messages.

It also introduces the concept of a Task.

A task represents a unit of work.

For example:

Task ID:
12345

Task:
Check stock for product SKU-1001

Status:
working

Later:

Task ID:
12345

Status:
completed

This is useful when an operation takes time.

For example, imagine an AI agent asks another agent to generate a large financial report.

It might not finish immediately.

Instead of waiting for one HTTP request to remain open forever, the system can track the task.

The current A2A specification defines task lifecycle and supports patterns such as polling, streaming, and push notifications for longer-running work.

What Are Messages and Artifacts?

A2A separates communication from the actual output.

A Message can contain instructions, questions, answers, or status information.

An Artifact represents a concrete result produced by an agent.

For example:

Message:
"Please generate the customer's invoice."

        ↓

Task:
Generate Invoice

        ↓

Artifact:
invoice-10025.pdf

The artifact could be:

  • Text
  • JSON
  • File
  • Image
  • Structured data
  • Another type of generated output

This separation is useful when agents need to work with real business data and documents.

How Does A2A Communication Work?

A simplified flow looks like this:

1. Agent A discovers Agent B
             |
             v
2. Agent A reads Agent B's Agent Card
             |
             v
3. Agent A sends a task/message
             |
             v
4. Agent B processes the task
             |
             v
5. Agent B sends status updates
             |
             v
6. Agent B returns the result

For example:

Customer Agent
      |
      | "Check stock for SKU-1001"
      v
Inventory Agent
      |
      | "Task working..."
      v
Inventory Agent
      |
      | "Stock available: 25"
      v
Customer Agent

This looks simple, but standardized communication becomes very important when hundreds or thousands of agents are involved.

A2A and MCP: Are They the Same?

No.

This is one of the most important things developers should understand.

MCP and A2A solve different problems.

A simple way to remember it is:

MCP = Agent talks to tools/data

A2A = Agent talks to another agent

For example:

                 AI Agent
                 /      \
                /        \
              MCP        A2A
              /            \
             v              v
       Tools / Data      Other Agent

MCP can connect an AI application or agent with tools, APIs and resources.

A2A allows independent agents to communicate and collaborate.

The official A2A documentation explicitly describes MCP as agent-to-tool communication and A2A as agent-to-agent communication.

So I don't see A2A as a replacement for MCP.

In many real applications, we may use both.

A2A + MCP Together

Let's take a practical SaaS example.

Suppose I have:

Main Business Agent
        |
        | A2A
        |
        +------ Inventory Agent
        |            |
        |            | MCP
        |            v
        |         MySQL
        |
        +------ Invoice Agent
                     |
                     | MCP
                     v
                  Invoice API

Here:

A2A handles communication between agents.

MCP handles access to tools and resources.

This creates a much more interesting architecture.

For example:

User
 |
 v
Business AI Agent
 |
 | A2A
 +--------------------+
 |                    |
 v                    v
Inventory Agent    Invoice Agent
 |                    |
 | MCP                | MCP
 v                    v
MySQL               Invoice API

This is where I think agentic applications become much more interesting for SaaS developers.

A2A Example for a Laravel Application

Let's imagine I have a Laravel-based ERP.

I have the following services:

ERP
CRM
Inventory
Invoice
Booking

I could create specialized agents:

ERP Agent
CRM Agent
Inventory Agent
Invoice Agent
Booking Agent

Now the user says:

"Show me today's customers who booked a service but have not paid their invoice."

The main agent could coordinate the work.

Main Agent
    |
    +---- A2A ----> Booking Agent
    |
    +---- A2A ----> Invoice Agent
    |
    +---- A2A ----> CRM Agent

The Booking Agent finds today's bookings.

The Invoice Agent checks payment status.

The CRM Agent provides customer information.

The main agent combines the results and provides the answer.

This is much easier to understand when each agent has a specific responsibility.

Why This Architecture Is Useful

There are several advantages.

1. Specialized Agents

Instead of creating one giant agent, we can create smaller agents.

For example:

Inventory Agent

only needs to understand inventory-related operations.

This can make the system easier to maintain.

2. Different Technologies

One agent might be written in:

Python

Another might be:

Node.js

Another service could be:

PHP / Laravel

A2A provides a common communication model between them.

The protocol is specifically designed for agents built using different frameworks, languages and vendors.

3. Independent Development

The inventory team can maintain the Inventory Agent.

The accounting team can maintain the Finance Agent.

The CRM team can maintain the CRM Agent.

They do not need to build one huge AI application together.

4. Better Business Workflows

Many business operations involve multiple systems.

For example:

Lead
 ↓
CRM
 ↓
Quotation
 ↓
Inventory
 ↓
Order
 ↓
Invoice
 ↓
Payment
 ↓
Email

AI agents can potentially coordinate these steps.

What About Security?

This is where developers need to be careful.

Giving agents the ability to communicate with other agents also creates new security problems.

Imagine an agent has permission to:

Create Invoice
Delete Customer
Update Product
Send Email
Access Database

We should not simply give an AI unrestricted access to everything.

A production system should consider:

  • Authentication
  • Authorization
  • API permissions
  • Rate limiting
  • Input validation
  • Audit logs
  • Agent identity
  • Data isolation
  • Tenant isolation
  • Tool permissions
  • Human approval for sensitive operations

This becomes even more important in a multi-tenant SaaS application.

For example:

Tenant A
   |
   v
Agent A
   |
   X
   |
Tenant B Database

Agent A should never be able to access Tenant B's data just because another agent requested it.

So when building agentic systems, I would treat an AI agent like an application service with permissions, not like a trusted administrator.

Is A2A Only for Large Companies?

No.

You don't need hundreds of AI agents to understand A2A.

A small project can have two agents.

For example:

Customer Support Agent
          |
          | A2A
          v
Order Agent

The Support Agent receives:

"Where is my order?"

It can ask the Order Agent for the latest order status.

This can be useful even in a small SaaS application.

Where Can We Use A2A?

There are many possible use cases.

E-commerce

Shopping Agent
     |
     +-- Product Agent
     +-- Inventory Agent
     +-- Payment Agent
     +-- Shipping Agent

ERP

ERP Agent
   |
   +-- Sales Agent
   +-- Inventory Agent
   +-- Finance Agent
   +-- HR Agent

Customer Support

Support Agent
     |
     +-- Order Agent
     +-- Billing Agent
     +-- Technical Agent

Software Development

Development Agent
       |
       +-- Code Agent
       +-- Testing Agent
       +-- Security Agent
       +-- Documentation Agent

Travel

Travel Agent
     |
     +-- Flight Agent
     +-- Hotel Agent
     +-- Payment Agent

The interesting part is that these agents don't necessarily need to be built by the same company.

That is one of the major reasons interoperability matters.

Is A2A Ready for Production?

A2A is no longer just a theoretical concept.

The current official specification is at version 1.0.0, and the protocol documentation provides concepts and mechanisms for agent discovery, task management, messaging, artifacts, streaming, authentication and asynchronous communication.

There is also significant industry interest in standardizing communication between agents.

In August 2026, A2A has received renewed attention around its governance and the broader Agentic AI Foundation ecosystem, showing that agent interoperability is becoming an important industry topic rather than just an experimental idea.

But I would still recommend developers start with a small proof of concept before moving a highly autonomous agent system into production.

A2A Is Not a Replacement for APIs

This is another important point.

I don't think REST APIs are going away because of A2A.

Normal APIs are still excellent for predictable application-to-application communication.

For example:

GET /api/products
POST /api/orders
GET /api/customers/123

A2A becomes useful when the communication involves agents, capabilities, tasks, delegation and collaboration.

So a real application may contain all of these:

Frontend
   |
REST API
   |
Laravel Application
   |
   +---- MySQL
   |
   +---- External APIs
   |
   +---- MCP
   |
   +---- A2A

These technologies can work together.

A Simple Way to Remember A2A

If you are a developer and want to remember the difference, use this simple rule:

REST
↓
Application communicates with application

MCP
↓
Agent communicates with tools/data

A2A
↓
Agent communicates with agent

This is not the complete technical definition, but it is a very useful mental model.

What Will Happen Next?

I think the most interesting part of agentic AI is not going to be one AI agent becoming smarter.

It will be multiple specialized agents working together.

Imagine this:

                 User
                   |
                   v
             Main AI Agent
                   |
       +-----------+-----------+
       |           |           |
       v           v           v
     CRM        Inventory    Finance
    Agent        Agent        Agent
       |           |           |
       +-----+-----+-----+-----+
             |
             v
        Business System

Each agent can have its own tools, permissions, memory and responsibilities.

MCP can help agents work with tools and data.

A2A can help agents communicate with each other.

Together, they can become an important part of future AI application architecture.

Final Thoughts

A2A is an important protocol to understand if you are building AI applications in 2026.

The main idea is actually quite simple:

AI agents should be able to communicate with other AI agents without requiring every developer to create a custom integration.

For developers, this opens an interesting architecture.

Instead of building one huge AI system, we can build smaller specialized agents and allow them to collaborate.

For example:

CRM Agent
Inventory Agent
Booking Agent
Invoice Agent
Support Agent

can work together to complete a larger business workflow.

And this is where A2A becomes interesting.

If MCP is helping agents connect to the tools and data they need, A2A is helping agents connect to the other agents they need.

For anyone building SaaS, ERP, CRM, e-commerce or automation software, I believe understanding this architecture now is useful because the way we build software is slowly moving from simple API calls toward systems where software agents can discover, delegate and collaborate with each other.