Coding Agent System

Claude Code is an agentic coding tool that lives in our terminal and understands our codebase. It helps us code faster by executing routine tasks, explaining complex code, and handling Git workflows—all through natural language commands. Let’s explore how Claude Code exemplifies the patterns we discussed throughout the course to see how coding agents aren’t anything different, and still follow the underlying trends of agentic AI.

Claude Code, rather than following a predetermined script, dynamically determines what actions to take based on our natural language requests and the current state of our codebase. Whether we ask it to “fix the bug in the authentication module” or “refactor this class to use dependency injection,” Claude Code must interpret our intent, analyze the codebase, plan a series of actions, execute them, and verify the results.

Let’s examine whether coding agents like Claude Code implement the patterns we discussed in the course and how they do it.

Is there any prompt chaining work in coding agents?

We do know that prompt chaining involves breaking down complex tasks into sequential steps, where each step builds upon the previous one’s output. Prompt chaining generates a document and then translates it to a separate language as a second LLM call, demonstrating this pattern’s basic structure.

When we ask Claude Code to implement a new feature, it won’t attempt to do everything in one massive operation. Instead, it will chain together multiple focused interactions. For example, first, it analyzes our existing codebase structure. Based on the analysis, it creates an implementation plan. Finally, it sequentially executes planned changes and validates the implementation.

Each phase uses the output from the previous phase as context, creating a robust chain of reasoning and action that mirrors human development practices. Here is a question for you: ...