Foundations of AI Agents: Tool and Function Calling

Language models are incredible at processing text, but they become truly transformative when they can interact with the outside world. If you want to move beyond basic chat interfaces, you need to understand how AI agents operate. This course teaches you how to bridge the gap between text generation and functional execution. You will discover how to design AI agents capable of understanding user requests, deciding which external tools to use, and executing those tools reliably to solve real-world problems. What you will learn: Understand the foundational concepts of AI agents and autonomous workflows. Learn how to structure function calling schemas for modern language models. Practice connecting LLMs to external APIs to retrieve live data and trigger actions. Build reliable tool-calling loops that handle errors and unexpected outputs gracefully. Implement prompt engineering techniques to guide agent decision-making. Explore modern agentic patterns, including basic Retrieval-Augmented Generation (RAG) concepts. The course begins with essential terminology and the core architecture of an AI agent. From there, you will read through step-by-step written explanations and analyze practical code snippets that demonstrate how to link language models with external functions. Designed for beginners with basic programming knowledge, this course requires no prior machine learning experience. Start building AI systems that do more than just chat, and teach them to take action today.