Designing AI Agents

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, governance—with six topologies—chain, route, parallel, orchestrate, hierarchy, 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 impose 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.