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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

In this chapter, we took a deep dive into the operational flow of agentic AI systems driven by large language models. We introduced the core agent loop – sense, think, act – and illustrated how it operates in frameworks such as AutoGen, where the LLM dictates the agent’s behavior through tool calls. We explored how agents use memory, goals, and state management within the constraints of the LLM’s context window, and how these responsibilities are handled by the surrounding AI framework or system.

We also examined the importance of planning and reasoning mechanisms, both in single-agent and multi-agent systems. With the rise of specialized reasoning models and the growing complexity of agent workflows, structuring interactions and decomposing tasks become essential for robust behavior. Then, we turned our attention to tool use, which is a critical enabler of agentic capability. After all, even the most intelligent LLM can observe or impact the...

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