Book
作って学ぶAIエージェント
laiso
Summary
本書はTypeScriptとBunを用いて、LLM APIとの接続、ファイル操作やシェルコマンドを扱う ツール、推論ループ、Git操作、GitHub連携までを章ごとに積み上げながら実装する実践書。 GitHubのIssueを起点にコード修正からプルリクエスト作成までを自動化する「Nano Code」 というコーディングエージェントを自作する過程を通じて、AIエージェントを「使う」側から 「作る」側の視点へと引き上げる。Pythonや機械学習の知識は前提とせず、Webエンジニアの 経験を延長する形で読み進められる構成になっている。
Target Readers
- AIエージェントの内部構造をコードレベルで理解したいTypeScriptエンジニア
- LLMを使ったツール・自動化開発に取り組みたいバックエンドエンジニア
- 既存のAIエージェントを「使う」だけでなく自作して学びたい開発者
Tags
Colophon
- Publisher
- 技術評論社
- ISBN
- 978-4-297-15565-0
- Published
- Apr 2026
- List price
- ¥3,520incl. taxMay differ from the actual selling price on Amazon
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Prerequisites
- Recommended
コンテキストエンジニアリング
蒲生弘郷
Reason: Understanding how to call LLM APIs and the basics of context design lets you keep sight of what's happening behind the reasoning loop and tool calls when building an agent from scratch in TypeScript. The context-engineering knowledge underpins the design decisions behind a self-built coding agent like Nano Code.
Sources
- Recommended
開発効率をアップする! Claude Code 実用入門
大澤文孝
Reason: Once you can use an AI coding agent like Claude Code effectively, you start wanting to build one yourself to see what's happening inside. Building AI Agents walks through implementing everything from LLM calls to tool execution and Git/GitHub integration, shifting your perspective from using such a tool to building one.
Sources
- Recommended
実践Claude Code入門
西見公宏、吉田真吾、大嶋勇樹
Reason: Once you've reframed Claude Code as an 'agent that executes agreements' and internalized the spec-driven development pattern, you naturally want to see with your own hands what happens inside such an agent. Building AI Agents pushes you from being a user to a builder by having you implement the reasoning loop and tool calls in TypeScript yourself.
Next Books
- Recommended
Building Applications with AI Agents
Michael Albada
Reason: Having built a coding agent from scratch in TypeScript, you internalize the inner workings of the reasoning loop and tool integration. Practical AI Agent Development builds on that implementation sense, broadening the view from a single agent to multi-agent systems and production operation.
Sources