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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

1 (1)
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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1
Part 1: Foundations of Agentic AI
5
Part 2: Building Your Own Agentic AI Framework
10
Part 3: Constructing Multi-Agent Systems
17
Index

5

Implementing Custom Tools

In the previous chapter, we explored the backend architecture of the AI-6 framework in detail, focusing on how the engine orchestrates the agentic loop, manages memory and session tate, and integrates with multiple LLM providers and tools. We saw how tools and models are dynamically discovered and how the engine handles their coordination through a unified abstraction. This foundation enables flexible, provider-agnostic AI workflows with persistent memory, resumable sessions, and secure, extensible tool usage.

In this chapter, we will shift focus from the overall engine architecture to a deeper investigation of the tool system itself. While Chapter 4 treated tools largely as pluggable black boxes, here we will look inside and examine how generic tools are designed to be reusable, consistent, and portable across LLM providers. We will explore how tools can be defined with structured schemas, how arguments are validated, and how they are registered...

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Design Multi-Agent AI Systems Using MCP and A2A
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