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Full Description
Engineer intelligent web apps that combine context, retrieval, agents, and MCP with the architecture needed to scale in production
Key Features
Build production-ready AI applications with Next.js, moving beyond simple LLM integrations and prototypes
Combine RAG, hybrid search, context engineering, LangGraph, and multi-agent workflows to create intelligent, context-aware apps
Build and integrate MCP servers and take productionize AI applications with real-time streaming, deployment, security, and observability
Book DescriptionBuilding an AI prototype is easy. Turning it into a scalable, reliable application is much harder.
Building Agentic AI Apps with Next.js and TypeScript shows you how to build production-ready AI applications using the web technologies you already know. Using Next.js and TypeScript, you'll create context-aware experiences, build RAG and hybrid search pipelines, orchestrate multi-agent workflows with LangGraph, and connect applications to reusable capabilities through MCP.
Through practical implementation, you'll learn how these technologies work together within a maintainable full-stack architecture and how to take your AI application from development to production with real-time streaming, testing, security, deployment, and observability.What you will learn
Architect maintainable, scalable AI applications using Next.js and TypeScript
Build context-aware applications that manage conversational state and relevant contextual data
Implement RAG and hybrid search pipelines to ground AI responses in reliable information
Design and orchestrate multi-agent workflows with LangGraph, including shared state, retries, and resilient execution
Build, package, and integrate MCP servers with Next.js applications
Deploy and operate AI applications with real-time streaming, testing, security, and monitoring
Who this book is forThis book is for JavaScript and TypeScript developers, full-stack developers, software engineers, architects, and technical leads who want to build sophisticated AI-powered applications using the web technologies they already know.
It's particularly suited to developers who have experimented with LLM APIs or built simple AI features but now want to understand how RAG, context engineering, MCP, and multi-agent architectures come together in a real application.
Contents
Table of Contents
Building The Foundation App
Keeping It Clean, Scalable and Testable
Our first AI Agent (Agent and Generative UI)
Designing Context-aware AI Apps
RAG In Practice
Performance Is Everything
Orchestrating LangGraph For Multi-Agent Collaboration
Building Your STDIO MCP Server
Building HTTP Streamable MCP Servers With Next.js
Building A Client And Consuming Your MCP Server
Deploying the Next.js Application on Vercel
Appendix 1: Advanced TypeScript for Agentic Development
Appendix 2: Working with AI Programming Agents
