Building AI Agents from Scratch

AI agents are transforming how we interact with technology, but understanding how they actually make decisions can feel like a black box. This course demystifies the underlying architecture of autonomous systems by guiding you through building an agent from the ground up. You will transition from writing simple static scripts to developing dynamic systems that can plan, reason, and execute tasks independently. By reading through clear explanations and structured code examples, you will master the foundational mechanics that power modern agentic workflows. What you'll learn: - Understand the core architecture of AI agents, including reasoning loops and state management. - Build custom tool schemas that allow your agent to interact with external APIs. - Implement secure file system actions to let your agent read and write data. - Configure robust error handling and fallback mechanisms for unexpected LLM outputs. - Apply modern Python development practices, including type hints and structured data validation. This course begins with essential definitions and the basic theory of agentic loops before moving into step-by-step implementation. You will explore how to structure prompts for reliable tool selection and how to safely manage the agent's environment. This course is designed for beginner to intermediate software developers and tech enthusiasts who want to understand the mechanics behind AI agents without relying on complex, bloated frameworks. No prior experience with AI orchestration libraries is required, though a basic familiarity with Python is helpful. Start reading today to build your first autonomous system from scratch.