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

Gigi Sayfan

Build a production-ready multi-agent AI framework from scratch using MCP and A2A to orchestrate powerful agent workflows

Key Features
Build Python-based AI agents without relying on third-party orchestration frameworks

Design production-ready multi-agent systems using A2A messaging

Integrate memory and context with MCP to create adaptive and stateful agentic AI frameworks

Book Description
Frustrated 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.

Who this book is for:
This 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.