By skyforbes 
Introduction
The first wave came with ChatGPT, a large language model in a consumer application, which gave everyone powerful new ways to generate and work with natural language. Now, we are moving into the next phase: AI agents
In just a couple of years, we’ve gone from asking chatbots to draft emails to building systems that act on our behalf. AI is no longer just something we talk to — it’s becoming something we collaborate with: systems that can search, plan, decide, and execute.
But while the idea of “agents” sounds exciting, building one can feel like stepping into a maze. You quickly run into questions like:
- How do I structure the agent’s reasoning?
- How should it interact with external tools or other agents?
- How do agents coordinate?
- How do I test these systems?
- How do I trace what happened when things go wrong?
This is where agent orchestration frameworks come in. These frameworks abstract away the low-level complexities of AI agent development and coordination, enabling developers to focus on high-level, business logic. They make it possible to build multi-agent systems quickly — without reinventing the scaffolding every time.
Among these, the OpenAI Agents SDK stands out as one of the most developer-friendly orchestration frameworks emerging in this space.
Instead of confronting you with custom configuration languages or labyrinthine class hierarchies, OpenAI Agents SDK ships a handful of clear-cut primitives — agents, runners, tools, handoffs, guardrails, and tracing — implemented in idiomatic Python. With those six building blocks, you can spin up a single-agent or multi-agent prototype within minutes.
Building Agents with OpenAI Agents SDK by Henry Habib is a practical guide to mastering this new paradigm. It takes a hands-on, example-based approach to not just understand AI agents in theory, but to have the right tools and intuition to build full, end-to-end agentic systems.
If you have any intention of building an agentic system — from a simple automation to a complex, multi-agent workflow — this book is a must-read before you get started.
Target Audience
✅ Anyone Looking to Build Agentic Systems
Whether you’re a machine learning engineer, software developer, or an engineering manager, this book provides a clear, structured approach to designing and implementing AI agents — even if your background isn’t in AI research.
✅ Anyone Looking to Automate Tasks
If your goal is to automate repetitive workflows — fetching data, integrating APIs, generating reports, or coordinating processes — this book is highly practical. With basic software development skills, you can learn to build agents that make decisions, call external tools, and adapt dynamically.
⚠️ For Those Looking to Deep Dive into LLMs
If you’re primarily interested in LLM internals, embeddings, prompt engineering, fine-tuning, or Retrieval-Augmented Generation (RAG), this book may not be the right fit. Building Agents with OpenAI Agents SDK focuses on agent orchestration and application development, not model mechanics. For a deeper exploration of how LLMs work, consider:
What Makes This Book Stand Out
Agentic Systems Basics
The book starts with the fundamentals of agentic systems: what an agent is, how it differs from a traditional chatbot, and why agents are such a powerful way in automating tasks across software, data, and business workflows.
Software has typically created deterministic (if X, then Y) and rigid systems that cannot address ambiguity or adapt to different goals — but this is changing. With the advancements of LLMs, intelligent systems are being created that can independently reason through steps and take actions to complete a goal.
The book frames an agent through three essential components: the model, the tooling interface, and memory and knowledge. It also simplifies and explains the idea of a “control logic framework,” which forms the backbone of an agent’s ability to think iteratively and respond dynamically as situations evolve. The explanations are clear and require no prior machine learning background.
OpenAI Agents SDK Core Primitives
OpenAI Agents SDK introduces a small set of core primitives that can be combined to build both single-agent and complex multi-agent systems. These include:
- Agent: A configurable wrapper around an LLM
- Runner: Manages the iterative process of reasoning, tool calls, and next steps
- Tool: The mechanism an agent uses to interact with external applications
- Handoff: How one agent delegates or transfers control to another
- Guardrails: Safety checks that validate user input and agent output
- Tracing: Captures detailed execution flows to enable debugging and observability
Think of primitives as LEGO bricks in a LEGO set. They’re the smallest standard pieces you use to build anything. The SDK might later include prebuilt recipes (like a LEGO instruction manual), but the bricks themselves are the primitives.
These primitives are explained clearly in the book, with examples that make it easy to see how they can be combined to build real systems.
Memory and Knowledge
Memory and knowledge systems allow agents to recall past context, retain important facts, and access external information sources when needed. This dramatically increases the usefulness and autonomy of agents.
The book explains short-term memory (or working memory), long-term memory, training knowledge, and retrieved knowledge in enough depth to give a clear understanding of how and when to apply each.
Personal Note: I found the section on “Structured Memory Recall” particularly interesting. Instead of storing every single message, the agent uses a tool that saves only the important information shared by the user. And instead of loading the entire conversation history, it retrieves and loads only the specific pieces of information that are relevant at that moment. This approach is fascinating as it mirrors how humans recall memory: selective, context-aware, and efficient.
Agent Design Patterns
This is not just SDK documentation presented in book form. It goes beyond features and introduces design patterns for building real agentic applications.
Depending on the use case, agents may require:
- Centralised orchestration
- Hierarchical coordination
- Decentralised workflows where multiple agents ideate collaboratively
- Parallel agentic compute
- Or a hybrid of these patterns
Each pattern is explained with clear and relatable examples, making it easier to build intuition about when to apply which structure.
Personal Note: The patterns around decentralised multi-agent systems are particularly interesting. The examples involving decentralised ideation and distributed reasoning are especially well-suited for creativity, brainstorming, optimization, and problem-solving workflows. These examples highlight how agents can genuinely co-create, rather than simply execute steps in sequence.
Logging, Tracking & Observability
A major challenge in agentic systems is not knowing why an agent made a certain decision. The OpenAI Agents SDK addresses this directly by treating tracing as a first-class primitive, rather than an afterthought or optional add-on.
The author emphasises this strongly, providing a clear explanation of traces and spans, which are the foundational components of the SDK’s tracing functionality. The book includes multiple examples showing how traces and spans can be customised and combined to build meaningful observability across different workflows.
As agentic systems grow more complex, debugging and introspection become increasingly important. The book acknowledges this reality and shows how to design observability in a way that scales with system complexity.
What Could Have Been Better
Agent testing
Agents can behave unpredictably and are inherently non-deterministic, meaning the same input may produce different outputs. This makes validating agent behavior significantly more challenging than testing traditional software systems. While the author does cover both end-to-end testing and unit testing approaches, the topic is complex enough that it could have benefited from deeper exploration. More guidance on handling hallucinations in tests, managing variability in responses, and frameworks or patterns for making agent tests reliable would have strengthened the book further.
Final Thoughts
Building Agents with OpenAI Agents SDK is an essential read for anyone beginning their agentic journey. It offers the right blend of theory, practical tools, and intuition to help you quickly start building both simple and complex agentic systems.
It’s a clear, well-structured read — each topic is explained concisely, with practical examples that make the concepts easy to internalize. The book ensures you not only understand how things work, but also when and where to apply them effectively.
Overall, AI agents are driving a shift from tools to partners. Instead of requiring users to adapt to software, the software adapts to the user, and the entire learning barrier and its related friction is removed.
Even though the space is evolving rapidly, the core principles outlined in the book — memory, tools, handoffs, and orchestration — will remain foundational for years to come, regardless of the framework. If you plan to build agentic systems and have time to read just one book, trust me, it should be this one.
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