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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

4

Building a Tool-Based Agentic AI Framework

In the previous chapter, we ventured beyond theory and observed an AI agent in action, specifically controlling a real Kubernetes cluster with a mix of autonomy, reasoning, and tooling. Through the concise yet powerful k8s-ai agent (https://github.com/the-gigi/k8s-ai), we demonstrated foundational AI agent capabilities such as observation, diagnosis, tool usage, maintaining a human-in-the-loop interaction, and employing an agentic loop. This agent, developed in just 61 lines of Python, is an impressive demonstration of the power of AI agents and LLMs.

In this chapter, we will start to develop a more complex and full-fledged AI framework called AI-6 (https://github.com/Sayfan-AI/AI-6/tree/v0.8.0). AI-6 will allow us to explore the full spectrum of AI agent capabilities, such as sophisticated memory management, advanced tool management facilities, and support for multiple LLM providers. AI-6 is highly unopinionated and highly extensible...

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Design Multi-Agent AI Systems Using MCP and A2A
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