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Building Agents with OpenAI  Agents SDK

Building Agents with OpenAI Agents SDK

By : Habib
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Building Agents with OpenAI  Agents SDK

Building Agents with OpenAI Agents SDK

By: Habib

Overview of this book

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.
Table of Contents (15 chapters)
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1
Part 1: AI Agents
5
Part 2: OpenAI Agents SDK
11
Part 3: Build AI Agents
13
Other Books You May Enjoy
14
Index

Multi-Agent Systems and Handoffs

There are certain tasks that an individual human cannot complete themselves. Instead, an organization of humans (i.e., a team) is needed. Think about any large company. The reason why these big enterprises can complete their objectives is because of an organization of different specialized human beings working together. In the same way, a one-agent system can only go so far. A multi-agent system, however, can do anything.

While a single agent can be highly capable, it has natural limitations. A lone agent often struggles when problems span multiple domains, require diverse expertise, or need parallel processing. For example, a customer support agent might handle simple inquiries well, but it may fall short when the task also involves financial analysis, legal reasoning, or technical troubleshooting. By distributing responsibilities across multiple specialized agents, you can achieve higher accuracy, scalability, and resilience than any single agent...

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