Code Agent Structure Analysis

This sharing will systematically guide everyone into the world of large model AI invocation. The first part introduces the basic principles and practices of AI invocation: starting from model selection, context windows, Token calculation, API authentication, and parameter configuration, combined with practical techniques such as streaming output, function calling, and structured output, helping everyone advance from "knowing how to call" to "knowing how to use it well." The second part delves into the implementation principles of code agents: using an open-source code agent as an example, it breaks down layer by layer its complete cycle of task planning, tool calling, code execution, error feedback, and self-correction, demonstrating how agents think and iterate like engineers. The third part, based on the above knowledge, shares a series of usage improvements: how to design high-quality prompt words, how to customize tools and permission boundaries for agents, and how to embed AI capabilities into one's daily workflows with a small amount of code, truly turning large models into levers for efficiency improvement. It is suitable for friends interested in AI application development and those who want to understand the internal mechanisms of agents.