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

Part 2

Building Your Own Agentic AI Framework

Part 2 walks you through building a real agentic AI framework (AI-6) from the ground up. You’ll explore the core engine that runs the agent loop, manages memory, sessions, tools, and multiple LLM providers, then dive into designing robust, provider-agnostic tools with schemas, validation, and execution flow. The part then shifts to user-facing interfaces, showing how to build Slack and web UIs that support visibility, control, and human-in-the-loop workflows. It closes by integrating the framework with the Model Context Protocol (MCP), enabling standardized tool discovery and interoperability across the broader ecosystem.

This part contains the following chapters:

  • Chapter 4, Building a Tool-Based Agentic AI Framework
  • Chapter 5, Implementing Custom Tools
  • Chapter 6, Creating Chat Interfaces Using Slack and Chainlit
  • Chapter 7, Integrating with the Model Context Protocol Ecosystem

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Design Multi-Agent AI Systems Using MCP and A2A
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