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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

Context management

The context refers to everything to which an agent has access. A good analogy is that the agent’s LLM is its brain, whereas the agent’s context is the information that is communicated to the brain to generate a response. We have already discussed several ways to expose important information to the agent, through system instructions, previous conversation history, prompt injections, and even through knowledge retrieval from tool calls.

In this section, we will narrow the context down to the local context (also called the run context). This refers to the information that is needed to instantiate the agent and acts as a dependency on tools and other hooks.

Local context

Local context enables your agent to access information (from when the agent was instantiated) without that data being explicitly part of the LLM’s prompt. This is most useful for storing user-specific information (e.g., user ID, name, preferences) so that tools can fetch...

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