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

6

Creating Chat Interfaces Using Slack and Chainlit

In the previous chapter, we explored AI-6's internal tool architecture—how tools are defined, registered, and executed. While a powerful backend is essential, the true potential of an agentic AI system is unlocked through interfaces that allow humans to effectively collaborate with autonomous agents.

User interfaces (UIs) in agentic systems serve a unique role. Unlike traditional applications, where the UI primarily captures input and displays output, agent interfaces must balance autonomy with control. They need to provide real-time visibility into what the agent is doing, allow interruptions when needed, surface debugging information during failures, and build trust through transparency. The challenge is designing interfaces that neither stifle the agent with excessive oversight nor leave users blind to its actions.

This chapter explores how AI-6 implements two complementary interfaces: a Slack bot for collaborative...

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