Goal
The path to "実践 AIエージェント開発"
Here is the reading path leading up to this book, derived from its dependencies and ordered from the fundamentals.
The path so far (12 books)
Why read this first: 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.
Why read this first: 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.
Why read this first: Having understood concurrency within a single process, you extend to a world where multiple processes—services—coordinate asynchronously. Microservices Patterns provides coordination patterns for distributed systems—messaging, sagas, event-driven design—extending the idea of concurrency across the network.
Why read this first: Once you grasp how LLMs process input and generate responses, plus the fundamentals of prompt design and context construction, you can see what abstractions a framework like LangChain takes off your hands. This book's insights on issues like Lost in the Middle and the volatility of conversation history give you criteria for designing context when building agents with LangChain.
Why read this first: 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
Why read this first: 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.
Why read this first: Once you can build applications with Spring, you start to feel the limits of framework-driven design centered on the database or the framework. Clean Architecture provides the Dependency Rule that keeps business rules independent of the framework, letting you choose a structure resilient to change over the long term.
Why read this first: Once you can design boundaries inside a system, you face the next decision: whether to split those boundaries into separate processes—services. Monolith to Microservices, guided by the pragmatic 'monolith first' principle, systematizes the motivations, methods, and pitfalls of decomposition.
Why read this first: Once you can decide whether to split a monolith, you stock up on the implementation patterns needed after the split. Microservices Patterns catalogs the established practices for service decomposition, data management, communication, and deployment, bridging from 'how to divide' to 'how to build.'
Why read this first: Learning microservice implementation patterns, you inevitably hit the hard problem of keeping data consistent across services. Designing Data-Intensive Applications supplies the theory of distributed-data consistency, replication, and transactions, underpinning why those patterns work.
Why read this first: Having understood the design of a single relational database, you broaden your view to the big picture of large-scale systems involving multiple data stores. Designing Data-Intensive Applications systematizes the fundamentals of data systems—replication, partitioning, consistency, faults—without being swayed by passing trends.
Why read this first: 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