Designing AI Agents with OpenAI: Memory, Reasoning, and RAG

AI is shifting from static chat interfaces to active, autonomous agents that can plan, execute, and learn. To build these next-generation systems, you need to understand how to program memory, reasoning, and tool integration. This text-based course guides you through the fundamental principles of agentic workflows, showing you how to design functional AI agents using OpenAI. By completing this course, you will understand how to transition from simple prompt-response interactions to stateful, autonomous systems capable of goal-directed behavior. You will learn to structure agent architectures that can make decisions and use external tools effectively. What you'll learn: - Understand the core architecture of intelligent agents, including planning, memory, and tool execution. - Implement Retrieval-Augmented Generation (RAG) to connect your agents with external knowledge bases. - Configure short-term and long-term memory structures to maintain context across multi-turn interactions. - Apply structured outputs and system prompts to guide agent reasoning and decision-making. - Design basic agent loops that can self-correct and execute API-based tools. - Practice debugging and evaluating agent behavior to ensure reliability and safety. We start with foundational definitions, key terminology, and the core concepts of agentic AI. From there, you will read through step-by-step conceptual breakdowns and clear, structured code examples, learning how to assemble these components into complete, working agents. This course is designed for software developers, tech enthusiasts, and curious builders who are new to AI engineering. No prior machine learning background is required; familiarity with basic programming concepts is all you need to begin. Start reading today to build your first autonomous AI agent.