Building Agent Tools: Function Calling for LLMs in Python

Large Language Models are incredibly capable, but their static training data limits what they can do on their own. By giving LLMs access to custom tools through function calling, you enable them to fetch real-time data, perform calculations, and interact with external databases and APIs. This text-only course guides you through the mechanics of transforming static text generators into dynamic, action-oriented AI agents. In this course, you will transition from basic prompting to building structured tool-use pipelines. You will learn the exact process of how LLMs declare, select, and execute Python functions, giving your applications the power to solve complex, multi-step real-world tasks through clear and structured text explanations. What you'll learn: - Understand the foundational concepts of tool use, function calling, and the agentic loop. - Write clean, type-hinted Python functions and generate precise JSON schemas using Pydantic. - Configure LLMs to recognize custom tools and return structured arguments. - Parse and execute tool calls safely within your Python application. - Implement error handling and validation strategies for when an LLM provides invalid tool arguments. - Apply best practices for secure tool execution to protect your system from unauthorized actions. This course begins with essential definitions of agents and tools before guiding you step-by-step through writing schemas, handling API responses, and managing the tool-execution loop. You will progress from simple utility functions to multi-tool orchestration through structured written explanations and code examples. This course is designed for beginner to intermediate Python developers who want to start building agentic AI applications. No prior experience with AI agents or advanced prompt engineering is required. Start reading today to unlock the full interactive potential of modern language models.