Intro To Google'S A2A Protocol: Interoperable Ai Agents

Master Google's A2A Protocol to build AI agents that discover, communicate, and collaborate using the official standards

What you'll learn

Explain the core concepts and architecture of the Agent2Agent (A2A) Protocol and how it enables interoperability between AI agents

Set up a Python development environment for A2A and implement the necessary components for an A2A-compliant agent

Design and implement Agent Skills and Agent Cards that effectively communicate an agent's capabilities to other systems

Build an Agent Executor that processes requests and generates appropriate responses according to the A2A protocol

Deploy and run an A2A server that can receive and handle requests from other agents

Implement streaming capabilities and multi-turn interactions to create more dynamic and contextual agent experiences

Differentiate between A2A and MCP (Model Context Protocol) and know when to use each in an agent ecosystem

Requirements

Basic Programming Knowledge fo Agents and Python

Python 3.13+

Description

Welcome to the most comprehensive course on Google's Agent2Agent (A2A) Protocol for technical developers and AI engineers.The A2A Protocol is revolutionizing how AI agents communicate and collaborate. Rather than building isolated agents that work independently, A2A enables the creation of interconnected agent ecosystems where AIs can discover each other's capabilities and work together seamlessly. This Google-backed standard is gaining significant traction as the foundation for truly interoperable AI systems.What You'll Learn in This Technical Deep DiveThis course takes you from the fundamentals of the A2A Protocol to implementing advanced agent interactions. You'll learn directly from the official A2A Protocol documentation and GitHub repositories, with practical examples that bring the concepts to life.Section 1: A2A Protocol FundamentalsUnderstand the core architecture and components of Google's A2A ProtocolExplore how A2A addresses the current fragmentation in the agent ecosystemCompare A2A with other standards, including the complementary Model Context Protocol (MCP)Learn the key differences between MCP vs A2A and when to use each in your systemsSection 2: A2A Development EnvironmentSet up a complete Python development environment for A2AInstall and configure the A2A SDK from the official GitHub repositoryNavigate the A2A Protocol documentation to find implementation guidelinesCreate your first basic A2A agent project structureSection 3: Agent Cards & Agent SkillsDesign effective Agent Skills that clearly communicate your agent's capabilitiesCreate comprehensive Agent Cards for discovery and interoperabilityImplement the A2A Protocol specifications for agent descriptionLearn best practices directly from the A2A Protocol GitHub examplesSection 4: The Agent ExecutorBuild the core logic that processes A2A requests and generates responsesImplement the execute and cancel methods according to A2A specificationsWork with RequestContext and EventQueue for efficient message handlingConnect your custom agent logic to the A2A Protocol interfacesSection 5: A2A Server DeploymentDeploy a fully functional A2A-compliant serverConfigure the DefaultRequestHandler and TaskStore for your agentExpose your agent to the ecosystem through proper endpoint configurationTest and debug your A2A server implementationSection 6: Client InteractionsSend requests to A2A servers using the client SDKProcess responses according to the A2A Protocol specificationImplement proper error handling for robust A2A client applicationsInteract with other agents in the A2A ecosystemSection 7: Advanced A2A FeaturesImplement streaming responses for real-time agent feedbackBuild stateful, multi-turn conversations between agentsIntegrate A2A with large language models like Google's GeminiCreate complex agent interactions with task state managementSection 8: MCP vs A2A - Complementary ProtocolsUnderstand the Model Context Protocol (MCP) and its relationship to A2ALearn when to use MCP for tool interactions vs A2A for agent-to-agent communicationBuild systems that leverage both protocols effectivelyDesign comprehensive agent ecosystems using the complete Google agent protocol stackBy the end of this course, you'll have practical experience implementing the A2A Protocol in real agent systems, creating both simple Helloworld agents and complex LLM-powered conversational agents that can stream responses and maintain context across multiple interactions.All examples and implementations are based directly on the official A2A Protocol documentation from Google and the reference code available in the A2A Protocol GitHub repository, ensuring you're learning the most up-to-date and accurate implementation techniques.Join thousands of developers who are building the future of interoperable AI with Google's Agent2Agent Protocol. Enroll now and start creating agents that don't just work in isolation, but form part of a connected, collaborative AI ecosystem.

Overview

Section 1: Introduction

Lecture 1 Intro to this course

Lecture 2 Introduction to A2A

Lecture 3 Resources & Github repo links

Lecture 4 Setting up Environments & Requirements

Section 2: Hand's on Agent2Agent (A2A) Example

Lecture 5 Agent Skills & Cards

Lecture 6 Agent Executor

Lecture 7 Starting your A2A Server (Sample Hello World Project)

Lecture 8 Interact with our A2A Server with our A2A Client

Section 3: Advanced Example

Lecture 9 Langgraph Example - Currency Converter Agent - Multi-turn, Streaming Agents

Lecture 10 A2A vs MCP

Section 4: Summary

Lecture 11 Congratulating on completing this course!

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