Master OpenAI's Agents SDK to design production-ready AI agents and agentic systems that solve real-world problems with practical guidance
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Key Features
- Gain a complete understanding of the OpenAI Agents SDK features including models, tools, memory, guardrails, orchestration, tracing, and multi-agent systems
- Progressively build AI agents through several hands-on projects that evolve from a simple workflow to a complex multi-agent system
- Implement advanced agent capabilities such as RAG, MCPs, administration, workflow integration, and much more
Book Description
Everyone’s talking about AI agents, but how do you build one that works in the real world? Not a toy demo, but an agent that solves real problems, saves time, and integrates into workflows. With vague frameworks, fragmented tooling, and endless hype, most developers are left without a clear path. The hardest part isn’t technical; it is knowing where to start.
This book gives you that starting point. It’s a complete guide to building intelligent AI agents and agentic systems using the official OpenAI Agents SDK. It begins by grounding you in the core concepts, design principles, and architecture of AI agents, how they differ from other traditional systems, their advantages, and why that matters.
Through practical step-by-step projects, you’ll master every feature of the SDK—tools, memory, RAG, multi-agent orchestration, tracing, handoffs, and more—while contributing to an end-to-end agent system that grows in complexity. Projects include a custom support agent, invoice and inventory assistant, health advisor, sales trainer, and data analyst, giving you production-ready skills.
By the end, you’ll know how to design, build, and deploy agentic systems that interact with APIs, query databases, hand off to external systems, and drive meaningful outcomes. You won’t just understand AI agents; you’ll be ready to ship them.
What you will learn
- Understand the core principles of AI agents and why they matter
- Use the OpenAI Agents SDK to build real, working agents from scratch
- Design both single-agent and multi-agent systems
- Integrate external tools, APIs, and data sources to extend agent capabilities
- Add memory and stateful context to your agents so they can “remember” and adapt over time
- Coordinate agent-to-agent handoff orchestrations
- Secure, monitor, and scale agents in production
Who this book is for
This book is for LLM engineers, developers, tech-savvy professionals, analysts, and consultants who want to build practical agentic AI solutions using the OpenAI SDK. A basic understanding of Python and AI concepts is recommended, but no prior experience with agents is required. Whether you're exploring agents for the first time or want to deepen your skills with hands-on projects, this guide provides structured, production-ready knowledge.
Table of Contents
- Introduction to AI Agents
- Introduction to OpenAI Agents SDK
- Environment Setup and Developing Your First Agent
- Agent Tools and MCPs
- Memory and Knowledge
- Multi-Agent Systems and Handoffs
- Model and Context Management
- Agent System Management
- Building AI Agents and Agentic Systems
Henry Habib advises F500 companies on analytics and operations, with a particular focus on building intelligent AI-driven solutions, tools, and agents to create impact and improve productivity. He teaches online courses on how to build AI agents and automations for work, as well as other Gen AI topics, amassing a network of more than 400K students. He also facilitates technical programs at businesses and governmental organizations. A proponent of the no-code and Gen AI revolution, he believes that anyone can now create powerful and intelligent applications to automate their work without any deep technical skills. He also runs The Intelligent Worker, a platform for teaching working professionals how to be more productive with Gen AI, no-code, automation, and other technologies, with courses and a free newsletter. He is also a speaker on Gen AI to many large F500 organizations. As an instructor, Henry is passionate about teaching students how to succeed on any topic related to Gen AI, automation, no-code, data, and productivity. He does this by creating courses with engaging and helpful content and always being around to answer any questions. Henry resides in Toronto, Canada, with his wife, and enjoys building random AI apps and playing tennis in his free time.
