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Building AI Agents with Typescript and Next.Js
Paperback
Publisher: Independently Published
Published: Sep 24 2026
Pages: 202
Weight: 0.73
Height: 0.43 Width: 6.69 Depth: 9.61
Language: English
Most books on AI agents are written for Python. This one is written for the millions of JavaScript and TypeScript developers who are being asked to build AI features inside the web applications they already own.
Building AI Agents with TypeScript and Next.js is a practical, production-focused guide to designing, building, testing, and deploying AI agents using the stack you already know. It begins with a clear answer to a question every JavaScript developer should ask: why is TypeScript more powerful than JavaScript, and why does that matter so much for AI? An agent turns unpredictable model output into real actions, such as database writes, refunds, and emails. The places where that happens are where agents break, and TypeScript, paired with runtime validation, is exceptionally good at guarding them.
From there, the book builds a complete agent from first principles. You won't be hidden behind a framework: you write the model interface, the tool layer, and the agent loop yourself. That way, you understand exactly how every piece works and can debug it when something goes wrong.
What you'll learn:
- Why TypeScript beats JavaScript for agent engineering, with twelve concrete advantages and a practical migration path.
- The type-system features that matter most for agents: discriminated unions, generics, Zod schemas, branded types, and result types.
- How to call models, stream responses, and get reliable structured output.
- How to design type-safe tools with permissions, human approval, and safe retries.
- How to build a resumable agent loop that can pause for a human and pick up where it left off.
- How to stream agent output to a Next.js chat interface using route handlers and server actions.
- Memory, retrieval-augmented generation (RAG), and vector search with Postgres and pgvector.
- Multi-agent patterns, long-running workflows, and the Model Context Protocol (MCP).
- Testing and evaluating agents, prompt-injection defences, security, and cost control.
- Deployment with Docker, AWS, Vercel, and CI/CD, plus monitoring in production.
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