Build agentic web systems that move beyond fragile scraping, scattered APIs, and isolated chatbots.
Modern AI teams need more than prompts. They need agents that can discover tools, query structured web data, coordinate with other agents, route tasks safely, and process live information without breaking every time a website changes. How do you design that kind of system without creating a brittle mess of scripts, tokens, and unreliable automation?
Designing Multi-Agent Systems with NLWeb and MCP gives developers, architects, and AI engineers a practical blueprint for building interoperable networks of specialized AI agents using Microsoft NLWeb, Model Context Protocol, structured retrieval, tool routing, and production-ready orchestration patterns. The manuscript positions NLWeb and MCP as core building blocks for machine-to-machine web interaction, with coverage of MCP server architecture, NLWeb ask workflows, vector pipelines, specialized retrieval agents, AgentFinder, DataFinder, multi-agent handoffs, cost engineering, and enterprise guardrails.
Inside, you'll learn how to:
Set up reliable Python and Node.js environments for agent systemsBuild MCP servers, tools, resources, prompts, and structured payloadsConnect NLWeb-style query flows to semantic search and live data sourcesDesign specialized agents for search, comparison, routing, and task handoffImprove cost, latency, token efficiency, validation, and production safetyApply security gates, human approvals, and deployment-ready guardrailsThis book is for software engineers, AI engineers, system architects, backend developers, and technical leaders who want to build practical agentic web infrastructure instead of one-off automation scripts.