finelybook
Designing AI Agents Principles, patterns, and best practices
Author(s): Jia Huang (Author)
- Publisher Finelybook 出版社: Manning Publications
- Publication Date 出版日期: MEAP began May 2026 Last updated June 2026 Publication in Early 2027 (estimated)
- Language 语言: English
- Print length 页数: 375 pages
- ISBN-13: 9781633433632
Book Description
AI agents promise to automate work on an unprecedented scale—even for tasks requiring reasoning and complex multi-step processes. But where do you start? How do you budget your dev time and token spend? What do you do with an agent that “almost works?” How do you scale or improve a multi-agent system? Designing AI Agents answers all these questions and more. In this enlightening book, author Jia Huang, a senior AI researcher at the Singapore-based Agency for Science, Technology and Research (A*STAR), presents an innovative two-axis framework blending seven cognitive functions—perception, memory, reasoning, action, reflection, collaboration, and governance—with six topologies—chain, route, parallel, orchestrate, hierarchy, and loop.
In Designing AI Agents, you’ll learn how to establish agent architectures that manage costs and take governance seriously from day one. This innovative book explores 27 reusable patterns that you can apply to your own agentic systems confidently. Each pattern has been stress-tested at scale, with over 10,000 engineers applying them to ship production agents in banks, manufacturers, and AI startups. You’ll appreciate how this book guides you toward system and harness design that imposes certainty and reliability on the non-deterministic behavior of LLM-driven agents. Once you catch author Jia Huang’s vision, you’ll stop asking “which tools?” and start asking “which patterns?”.
Unlike other “agentic patterns” books that dwell on abstract theory, every chapter in this practical guide grows a single running example. You’ll incrementally build Argus, a code-review agent that evolves from a 50-line reasoning-and-action loop all the way to a production-grade system. As you steadily upgrade Argus, you’ll learn both which patterns to use and when and why to apply them. Plus, full case studies exploring agents working as a DevOps incident responder, compliance reviewer, and research synthesis agent show the methodology in action across diverse domains. And, as with all Manning books, you’ll find a clearly defined learning path, thoughtfully edited and readable text, and our promise that everything is accurate and reliable.
table of contents
1 The agent paradigm shift
2 Agent architecture and the two-axis map
3 Perception: What your agent sees determines what it does
4 Memory: What your agent remembers shapes what it becomes
5 Reasoning: How your
