The article outlines a three-pillar framework for integrating large language models (LLMs) with external tools, enhancing their capabilities beyond static responses. Tool calling is a key concept here, allowing LLMs to access real-time data, perform calculations, and execute actions. Unlike pre-built systems like ChatGPT, which come with fixed tools, custom agents can be tailored to specific needs by integrating various APIs or databases. The first pillar focuses on data access tools, which are read-only and retrieve information from external sources. It explains how tools like vector databases are useful for semantic searches, while SQL and NoSQL databases handle structured queries effectively. Graph databases are highlighted for their ability to manage relationships between data points, making them ideal for complex datasets. The second pillar addresses computation tools that process and transform data. These include code execution tools for running custom calculations, mathematical operations for complex analyses, and data transformation tools that convert data formats. The article also mentions machine learning model inference for specialized tasks, along with media processing tools for handling various media formats. The final pillar centers on action tools, which execute changes and trigger workflows. This includes communication tools for sending notifications, workflow automation tools for managing tasks, and data manipulation tools that alter database records. The article emphasizes the importance of caution with these actions, outlining strategies like read-before-write and conditional actions to mitigate risks. An example illustrates how these pillars interact in a customer service scenario, demonstrating the practical application of the framework.