11 Model Context Protocol and multi-agent AI systems

 

This chapter covers

  • Model Context Protocol (MCP) and how it solves the N×M integration challenge in AI systems
  • Implementing specialized agents for search, policy knowledge, and vision processing in e-commerce applications
  • LangGraph state management, conditional routing, and workflow orchestration for complex multi-agent systems
  • Adding vision capabilities to multi-agent systems
  • Comprehensive testing strategies for multi-agent workflows, including blended queries and failure scenarios

A customer texts a photo of their hiking boots and writes:

“Do you have something similar but waterproof?”

Seconds later, they add:

“What’s your return policy if they don’t fit?”

This is real-world complexity: image understanding, product search, policy lookup, all in one flow.

This simple interaction requires image analysis, product search, policy retrieval, and response coordination—exactly the kind of real-world complexity that breaks most AI systems.

Now, as you reach this final chapter, you’re ready to tackle one of the toughest—and most exciting—challenges in AI development today: turning your powerful AI models into fully integrated, multimodal, multi-agent systems that thrive in complex production environments.

11.1 What is MCP?

11.1.1 The hidden complexity: The N×M problem

11.1.2 The solution: Model Context Protocol (MCP)

11.2 Building your first real MCP tool with a CSV-powered product catalog

11.2.1 Loading your product catalog

11.2.2 Setting up the MCP server

11.2.3 Running and testing your server

11.3 Teaching your AI to use MCP tools

11.3.1 How models learn what tools they can use

11.3.2 A complete example: model → MCP → answer

11.3.3 What you didn’t have to write

11.3.4 Tool design becomes interface design

11.4 Multi-agent systems with LangGraph

11.4.1 Why multi-agent architecture matters

11.4.2 Introducing LangGraph: The multi-agent framework

11.4.3 Building intelligent agent coordination

11.4.4 Real-world example: ShopBot multi-agent system

11.4.5 Running your multi-agent system

11.4.6 Adding vision: Image-based product search with VisionAgent

11.4.7 What we built—why it matters

11.5 Testing multi-agent workflows—not just agents

11.5.1 Blended queries: “Can your system walk and chew gum?”

11.5.2 Ambiguous intent: “Does your system make the right call?”

11.5.3 3. Agent failure: “Do you fail gracefully?”

11.5.4 State flow and memory: “Does information flow, or get lost?”

11.5.5 Regression: “Are you moving forward—without breaking what worked?”

11.6 From playground to production: How real AI gets built

11.6.1 From prompts to production workflows