Book
Building Applications with AI Agents
Michael Albada
Summary
シングルエージェントからマルチエージェントシステムへの設計・実装を、コアコンセプト・ オーケストレーション・評価・本番監視・セキュリティ・人間との協働まで一貫して解説する実践書。 AIエージェントアプリケーション構築のライフサイクルを体系的にカバーする。
Target Readers
- LLMを用いたAIエージェントシステムを設計・実装するバックエンドエンジニア
- マルチエージェントアーキテクチャの本番運用を検討しているアーキテクト
Tags
Colophon
- Publisher
- オライリー・ジャパン
- ISBN
- 978-4-8144-0159-8
- Published
- Apr 2026
- List price
- ¥4,620incl. taxMay differ from the actual selling price on Amazon
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Prerequisites
- Recommended
Designing Data-Intensive Applications
Martin Kleppmann
Reason: Having grasped the theory of consistency, reliability, and scale in large-scale data systems, you see it as the foundation for putting AI agents into production. Practical AI Agent Development bridges the robustness principles learned in data-intensive systems to designing agent state management and tool integration.
Sources
- Recommended
LangChainによるAIエージェント開発講座
神田良輝
Reason: Having gone through the basic design and implementation patterns of agents with LangChain, you start to see the design challenges of multi-agent systems that go beyond a single agent. Practical AI Agent Development builds up production-facing perspectives—orchestration, evaluation, monitoring, security—that framework learning alone doesn't cover.
- Recommended
作って学ぶAIエージェント
laiso
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
- Related
Building Microservices
Sam Newman
Reason: Learning to design 'autonomous parts that coordinate' with microservices, the idea carries over to multi-agent design that coordinates several AI agents. Practical AI Agent Development becomes an entry point for applying the sense of loose coupling, communication, and responsibility division cultivated in service decomposition to AI systems.