Building Agentic AI with MCP: How to Design Production-Ready AI Agents, Multi-Agent Systems, and Scalable Tool Integrations
English | September 21, 2026 | ASIN: B0HKJL4HYH | 232 pages | Epub | 438.54 KB
Most agent demos work until they touch production. Then the tools multiply, the context explodes, the wrong action fires, and nobody can reconstruct what happened.This book is the practical engineering guide to building agentic systems that survive that moment. It takes the Model Context Protocol-the open standard that has become the USB-C of AI tools-and shows you how to design high-signal tools, secure remote servers, wire multi-agent workflows, and ship with observability, regression tests, and human approval gates already in place.You will learn how to keep tool catalogues small enough for models to choose correctly, how to return only the data that matters, how to containerise and scale Streamable HTTP servers, how to measure selection accuracy with golden datasets, and how to turn real production failures into permanent test coverage. Three complete case studies-an internal knowledge-and-action agent, a multi-agent software delivery pipeline, and a customer-support agent with human-in-the-loop-walk you from architecture to runnable patterns you can adapt the same day.What sets this book apart is its refusal to stay conceptual. Every chapter is grounded in current SDKs, production checklists, and failure modes that teams actually hit. You finish with reusable project templates, a production-readiness gate, and the confidence that your next agent will be inspectable, policy-controlled, and ready for real users.Open the first chapter. Build the first working server in minutes. Then keep going until the system you ship is one you would trust in production.
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