Design Multi-Agent AI Systems Using MCP and A2A: Engineer your own Python-based agentic AI framework with tool use, memory, and multi-agent workflows

Build a production-ready multi-agent AI framework from scratch using MCP and A2A to orchestrate powerful agent workflowsFree with your book: DRM-free PDF version + access to Packt's next-gen Reader*Key FeaturesBuild Python-based AI agents without relying on third-party orchestration frameworksDesign production-ready multi-agent systems using A2A messagingIntegrate memory and context with MCP to create adaptive and stateful agentic AI frameworksBook DescriptionFrustrated by opaque agent frameworks that hide how things work? This book gives you complete control by guiding you through building a fully functional, extensible agentic AI framework in Python without relying on external orchestration tools.You’ll begin by implementing a simple tool-using agent, and then gradually extend its capabilities with structured tool schemas, user interfaces, and memory via the Model Context Protocol (MCP). From there, you’ll build collaborative multi-agent systems powered by Agent-to-Agent (A2A) messaging and deploy them in realistic environments. Along the way, you’ll explore secure tool invocation, message routing, observability, and human-in-the-loop workflows.With annotated code, deep engineering insights, and practical deployment patterns, this hands-on guide equips you to build AI agents that reason, plan, act, and adapt, whether you’re shipping production systems or experimenting with cutting-edge LLM-based architectures.Written by Gigi Sayfan, who builds AI agent infrastructure at Perplexity and is a bestselling author with decades of experience in AI and distributed systems, this book gives you the tools and knowledge to engineer your own advanced agentic systems.*Email sign-up and proof of purchase requiredWhat you will learnDesign and implement tool-using AI agents from the ground upBuild modular components for extensible agent frameworksCreate secure and observable tools with structured inputsIntegrate agents with chat UIs such as Slack and ChainlitLeverage MCP for context handling and agent memoryOrchestrate collaborative agent workflows using A2ADebug and deploy agents in production-like environmentsExplore future-ready agent capabilities and GenUX designWho this book is forThis book is essential for AI engineers, ML practitioners, and software architects building agentic systems with large language models. It’s also ideal for DevOps engineers and technical leaders seeking deep insights into building and scaling autonomous AI workflows. Python coding skills and basic familiarity with LLMs are recommended.Table of ContentsIntroduction to Generative AI and AI agentsUnderstanding How AI Agents WorkA Hands on Walk-Through of a Simple AI AgentBuilding a Tool-Based Agentic AI FrameworkImplementing Custom ToolsCreating Chat Interfaces Using Slack and ChainlitIntegrating with the Model Context Protocol EcosystemDesigning Multi-Agent SystemsImplementing Multi-Agent Systems with A2ATesting, Debugging, and Troubleshooting Multi-Agent SystemsDeploying Multi- Agent SystemsAdvanced Topics and Future Directions Read more