Complete Master Class on Agent to Agent (A2A) Protocol

Master Google's A2A Protocol to build AI agents. A to Z of Building Multi Agent System using A2A Protocol .

What you'll learn
- Learn Agent to Agent (A2A) communication Lifecyle and all Components and how this compares with MCP
- Understand A2A Protocol Agent cards , Agent discovery Process
- Understand A2A Protocol Events and Communication Flow
- Understand A2A protocol Core Objects , RPC Methods
- Master Google's Agent to Agent Protocol (A2A)
- Build Multi agent apps with Agent to Agent (A2A) Protocol With Tool Support
- Hands on Knowledge on A2A Protocol Client Server Implementation using Python and A2A SDK
- Build Multi agent apps with Agent to Agent (A2A) Protocol
- Build agent apps with Agent to Agent (A2A) Protocol and Protected Agent Card
- Build agent apps with Agent to Agent (A2A) Protocol with Support for Streaming Response
- We'll show how to set up free Gemini API Key, so you don't need to pay for AI Models when learning!
- Set up a Python development environment and build A2A-compliant agents
- Develop an Agent Executor to handle requests and generate responses using the A2A protocol
- Deploy an A2A server to receive and process agent-to-agent communication
- Distinguish between A2A and MCP protocols and their appropriate use cases in agent systems
- Get Basic Foundational Knowledge AI, LLM, AI Agents, etc.
- Create Agents with Lang graph React Agent method and Gemini LLM Tooling

Requirements
- Basic Python Knowledge is beneficial.
- Python 3.12 Installed on the Machine to run the Demo in your machine

Description
Description

Welcome to the most comprehensive course on Google's Agent2Agent (A2A) Protocol for AI Enthusiasts.

TheAgent-to-Agent (A2A) Protocolis changing the landscape of AI communication. Instead of building standalone agents that operate in isolation, A2A enables the development ofinterconnected agent ecosystems—where AI agents candiscover, understand, and collaboratewith one another in real time. Backed by Google and rapidly gaining momentum, A2A is emerging as thecore standard for interoperable AI systems.

What You'll Learn in This Technical Deep Dive

In this course, you’ll go beyond the theory and intopractical implementation. Starting with the fundamentals of the A2A Protocol, you’ll progress to advanced agent communication flows, working directly with examples inspired by theofficial A2A documentation. You’ll exploremultiple real-world agent implementationsand step throughlive demosthat clearly explain each concept, helping you build a strong foundation and the confidence to apply A2A in your own projects.

Why Take This Course?

Real-World Skills: Learn how A2A fits into future Agent system Implementation  protocols and the larger Multi agent AI Systems

Hands-On Projects: Set up client-server agent to agent pairs and execute communication flow, Secured Agent Communication, Multi agent with Tool calling in A2A Protocol.

Simple Explanations: Break down technical specs into digestible, practical steps followed with Technical Implementation Demo

Future-Proof Your Skills: Gain expertise in a fast-growing field relevant to Agent to Agent Protocol , Multi agent system development.

Section 1: Introduction to A2A Course

Course Outline

Why You should Learn A2A

Get to Know your Instructor

Notes about getting most out of this Course

What to do if you need help while following this course

Section 2: Introduction to AI Agents and A2A Protocol

Data Science in 3 Minutes

LLM Overview

A Little Secret: Quick Trick to Grasp All AI Concepts Easily

What is Tool or Function Calling

What is AI Agents

Section 3: Overview of A2A Protocol

A2A in One Sentence

What is MCP and How MCP Works

A2A Detailed Overview

A2A and MCP in Big Picture of Agentic AI Systems

Multi-Agent System using A2A Protocol

Section 4: A2A Protocol Basic Concepts

A2A Basics – Core Actors

A2A Basics – Simple A2A Communication Flow

A2A Basics – Agent Cards Explained in Detail

A2A Basics – Agent Discovery Mechanisms

Section 5: A2A Advanced Concepts – Communication Protocols

A2A: Core Objects & Events

JSON-RPC Methods in A2A Protocol

Agent-to-Agent Web Protocols (HTTP, POST, SSE, JSON-RPC)

A2A Authentication Mechanisms

A2A Detailed Communication Flow

Section 6: A2A Protocol Specification

Logical Concept vs. Technical Implementation

A2A Protocol Specification – Agent Discovery

Agent Card Resolver – SDK Implementation

(Optional) Why Covering All Specification in Theory Isn’t Ideal

Section 7: Setting Up Development Environment

Install Code Editor (Visual Studio Code)

Install Python (Windows/Mac)

Install Pip (Windows/Mac)

Install UV (Windows/Mac)

Starlette ASGI Service – API Host Introduction

Uvicorn Server Setup

Section 8: Building a Simple A2A Agent

Simple A2A Agent – Architecture Diagram

A2A Specification Implementation

Python Project Structure

Setting Up and Running the Simple A2A Agent

Code Walkthrough and Demo

Closing Notes

Section 9: Implementing an A2A Streaming Agent

Streaming Response Introduction

Python Specification Diagram

Running the Streaming Agent Demo

Code Walkthrough and Demo

Section 10: Implementing an A2A Protected Agent Card

Quick Demo of Protected Agent Card

A2A Specification for Protected Cards

Python Specification Diagram

Setup and Run the Protected Agent Demo

Code Walkthrough and Demo

Closing Notes

Section 11: Advanced Implementation – Multi-Agent with Gemini Flash & Tool Calling

Architecture Diagram

Quick Demo

Python Program Specification

Tooling Support for AI Agents

Tool Calling with Supported LLM

Getting a Gemini API Key

Setting up Gemini API Key in .env

Program File Structure

Code Walkthrough – A2A Client

Code Walkthrough – Server Config & Main File

Code Walkthrough – Agent Executor (Middleman)

Code Walkthrough – Remote Agent & Tool Implementation

Setting Up and Running the Demo

Final Demo & Output Review

By the end of this course, you'll have practical experience implementing the A2A Protocol in real agent systems, creating both simple agents to More  complex LLM-powered conversational agents that can stream responses and maintain context across multiple interactions.

All examples and implementations are based official A2A Protocol documentation from Google and the reference code available to download with course Materials, ensuring you're learning the  accurate implementation techniques.

Join thousands of developers who are building the future of interoperable AI with Google's Agent 2 Agent Protocol. Enroll now and start creating agents that don't just work in isolation, but form part of a connected, collaborative AI ecosystem.

Who this course is for:

Any One Who want to Know How A2A protocol works and Want to build one by yourself.

Software Engineers and Developers who want to build interoperable AI agent systems using standardized A2A protocols

AI/ML Engineers looking to extend their knowledge beyond model building to creating agent architectures

Technical Product Managers who need to understand how agent systems can be designed to work together

Solution Architects planning AI ecosystems that require collaboration between multiple agent systems

Technical Team Leaders who are evaluating implementation strategies for connected AI agent networks

Course Includes

3+ hours of video lectures

Downloadable code and resources

Lifetime access

Certificate of completion

Q&A support from the instructor

Requirements

Basic knowledge of Python

Python 3.12+ installed on your system

A willingness to learn something cutting-edge!

Get Started Today

Join the course and become one of the early developers skilled in implementing decentralized, secure, agent-to-agent communication.

Start building the future of AI and A2A Agents , one agent at a time.

Who this course is for:
- For All AI Enthusiasts
- Software Engineers and Developers who want to build interoperable AI agent systems using standardized A2A protocols
- Technical Product Managers who need to understand how agent systems can be designed to work together
- Solution Architects planning AI ecosystems that require collaboration between multiple agent systems
- Technical Team Leaders who are evaluating implementation strategies for connected AI agent networks
- AI/ML Engineers looking to extend their knowledge beyond model building to creating agent architectures
More Info