Building Applications with AI Agents

Preface

When I first started connecting language models, tools, orchestration, and memory together into what we now call an agent, I was surprised by how capable this design pattern was, and just how much confusion there was about this topic. During my time building agents and sharing my findings on incident investigation, threat hunting, vulnerability detection, and more, I found that this latest design pattern enabled us to solve whole new classes of problems, but also came with many practical hurdles to making them reliable for real-world applications. Engineers, scientists, product managers, and leadership all wanted to know more. “How do I get my agent to work?” “I can get my agent to work some of the time, but how do I get it to work most or all of the time?” “How do I choose a model for my use case?” “How do I design good tools for my agent?” “What kind of memory do I need?” “Should I use RAG?” “Should I build a single-agent or multiagent system?” “What architecture should I use?” “Do I need to fine-tune?” “How do I enable agents to learn from experience and improve over time?”

While there are many blog posts and research papers that focus on specific aspects of the topic of designing agent systems, I realized there were a lack of accessible, holistic, trustworthy guides for this. I couldn’t find the book that I wanted to share with my colleagues, so I set out to write it.

Through in-depth discussions, I’ve helped teams navigate the complexities of AI agents, considering ...