This tutorial from Machine Learning Mastery explains how to build an AI agent from the ground up in plain Python, with the Anthropic API serving as the model interface. The article frames the task as a learning exercise: understand what an agent is and then construct one by hand.
The central message is that agent technologies need not be opaque. By using plain Python and a direct API, the author shows that the core of an agent is a small amount of orchestration code around model calls. This makes the design concrete and debuggable.
The “why” matters as much as the “how.” Building from scratch helps developers see exactly where the agent’s behavior is defined, rather than accepting defaults from a framework. It also provides a mental model that can be carried into more complex agent projects.