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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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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

Summary

Let’s recap. We provided a comprehensive exploration of the AI-6 tool system, shifting the focus from the high-level orchestration engine to the internal structure and behavior of the tools themselves. We examined how AI-6 achieves provider-agnostic tool interoperability by defining a unified, generic tool specification that maps cleanly to provider-specific formats such as OpenAI’s function calling or Ollama’s tool schema. These tools are designed to be reusable, consistent, and easily translatable across different LLM APIs, with a robust schema and input validation mechanism at their core.

We continued with detailed walkthroughs of built-in command-line tools such as pwd, ls, awk, and a special-purpose echo tool for file writing. These tools are implemented using the CommandTool base class and are capable of performing file operations and shell interactions directly from within the AI framework. Additional tools, such as test_runner for executing...

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Design Multi-Agent AI Systems Using MCP and A2A
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