Building AI Agents with OpenAI Tools

Artificial intelligence is shifting from passive chat assistants to active agents that can execute tasks, use external tools, and solve complex problems autonomously. This text-based course provides a clear, step-by-step pathway to understanding and constructing these intelligent systems from scratch. Through clear written explanations, conceptual breakdowns, and practical code walkthroughs, you will learn how to transition from basic prompting to building agentic workflows that interact with the real world. You will gain a deep understanding of how language models can reason, plan, and make decisions when equipped with the right tools. What you'll learn: - Understand the core architecture, terminology, and lifecycle of autonomous AI agents - Implement OpenAI function calling to securely connect language models to external APIs - Apply advanced prompt engineering patterns to structure agent reasoning and execution loops - Integrate retrieval-augmented generation (RAG) patterns to give your agents access to custom knowledge bases - Manage agent state and build basic memory systems to maintain context over long interactions - Debug and optimize agent performance using modern testing and logging practices We begin by establishing a solid foundation of key agent concepts and tool-use terminology before moving into practical implementation. You will then progress through structured modules detailing tool definition, API integration, and agent coordination, all demonstrated through clean, readable code snippets. This course is designed for developers, tech enthusiasts, and curious beginners who have a basic familiarity with Python. No prior experience with artificial intelligence, machine learning, or agentic frameworks is required. Start reading today to build and deploy your first autonomous AI agent.