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  • Book Overview & Buying Building Agents with OpenAI  Agents SDK
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Building Agents with OpenAI  Agents SDK

Building Agents with OpenAI Agents SDK

By : Habib
4 (1)
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Building Agents with OpenAI  Agents SDK

Building Agents with OpenAI Agents SDK

4 (1)
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

Training knowledge

As discussed in Chapter 1, training knowledge refers to information that is inherently stored in the model through its training data. Every LLM begins with a vast repository of inherent knowledge derived from the massive datasets (typically, a large corpus of internet text) on which it was initially trained. The benefit of an LLM having internal knowledge is that the knowledge itself has the following advantages:

  • It is quickly retrievable: Since the inherent knowledge is “baked” into the model weights, the model can retrieve the information very quickly and is typically limited only by the LLM’s compute speed
  • It has a wide coverage: Since the training data is vast (the corpus of the internet), the inherent knowledge can cover lots of topics in fairly great detail

The process of changing the model’s inherent knowledge is called fine-tuning. Unlike prompting or retrieval-based techniques that guide a model’...

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