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  • Book Overview & Buying Design Multi-Agent AI Systems Using MCP and A2A
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Design Multi-Agent AI Systems Using MCP and A2A

Design Multi-Agent AI Systems Using MCP and A2A

By : Gigi Sayfan
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Design Multi-Agent AI Systems Using MCP and A2A

Design Multi-Agent AI Systems Using MCP and A2A

3 (2)
By: Gigi Sayfan

Overview of this book

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. *Email sign-up and proof of purchase required
Table of Contents (18 chapters)
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Part 1: Foundations of Agentic AI
5
Part 2: Building Your Own Agentic AI Framework
10
Part 3: Constructing Multi-Agent Systems
17
Index

Summary

In this chapter, we followed the remarkable journey of generative AI from the pioneering work of symbolic reasoning and expert systems to the meteoric rise of deep learning and transformer-based models. We explored how the field evolved from rule-based automation to data-driven intelligence, leading to our current sophisticated generative models, capable of not only understanding but also creating content.

We examined the emergence of AI agents and intelligent, autonomous systems that perceive, plan, act, and adapt to their environment. Unlike traditional chatbots, AI agents are equipped with memory, tool use, and multi-step reasoning abilities, enabling them to perform complex, dynamic tasks across diverse domains. We studied multiple architectures, saw how they differ from chatbots, and surveyed some real-world use cases, from autonomous coding assistants to AI companions.

At this point, you should have all the necessary background to understand the principle of...

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Design Multi-Agent AI Systems Using MCP and A2A
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