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 workflows

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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.

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What you will learn

• Design and implement tool-using AI agents from the ground up
• Build modular components for extensible agent frameworks
• Create secure and observable tools with structured inputs
• Integrate agents with chat UIs such as Slack and Chainlit
• Leverage MCP for context handling and agent memory
• Orchestrate collaborative agent workflows using A2A
• Debug and deploy agents in production-like environments
• Explore future-ready agent capabilities and GenUX design

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.

Table of Contents

1. Introduction to Generative AI and AI agents
2. Understanding How AI Agents Work
3. A Hands on Walk-Through of a Simple AI Agent
4. Building a Tool-Based Agentic AI Framework
5. Implementing Custom Tools
6. Creating Chat Interfaces Using Slack and Chainlit
7. Integrating with the Model Context Protocol Ecosystem
8. Designing Multi-Agent Systems
9. Implementing Multi-Agent Systems with A2A
10. Testing, Debugging, and Troubleshooting Multi-Agent Systems
11. Deploying Multi- Agent Systems
12. Advanced Topics and Future Directions

Gigi Sayfan is a member of the AI agents infra team at Perplexity, focused on building large-scale environments and harnesses for AI agents. He brings over 30 years of software development experience across domains, including instant messaging, chip fabrication process control, embedded multimedia for game consoles, brain-inspired machine learning, custom browser development, web services for distributed 3D game platforms, IoT sensors, and virtual reality. He has written production code in Go, Python, Java, C#, C++, and TypeScript/JavaScript. His expertise includes AI agents, generative AI, cloud-native technologies, DevOps, databases, networking, and distributed systems. Gigi has authored books and articles on Kubernetes and microservices.