How Do AI Coding Agents Work With Code in Multiple Files?

AI coding assistants are becoming common tools for software developers. A key capability is their ability to work with projects where the code is spread out across many different files and folders. This allows them to make intelligent suggestions and perform complex tasks that affect the entire application. Their ability to do this is not magic; it's a systematic process of analyzing your entire project to build a deep model of how everything works together.

Scanning the Code with Parsers

When an AI coding agent first looks at your project, its initial job is to read and process every single line of code in every file. To do this, it uses a special tool called a parser. Each programming language has a specific grammar and set of rules, and the parser is designed to understand that specific structure.

For each file, the parser builds something called an Abstract Syntax Tree (AST). This sounds complex, but it's really just a way of breaking down the code into its fundamental components, much like a sentence can be broken down into nouns, verbs, and adjectives. The AST represents the code as a tree structure, with nodes representing elements like function declarations, loops, and variable assignments. This tree makes the code's structure explicit and easy for a machine to analyze. By creating an AST for every file, the AI moves beyond seeing code as simple text and starts to see its grammatical structure.

Building a Semantic Code Graph

After parsing all the files, the AI's next step is to connect all these individual trees into a single, unified model of the entire project. This model is often called a code graph. This graph is much more powerful than the individual ASTs because it represents the semantic relationships between code elements across different files.

This means the AI links a function's definition in one file to every place it is called in all other files. It connects a class definition to every variable that is an instance of that class. For example, the AI understands that a User class defined in models.py is the same User type being used in api_handler.py and user_profile.py. It builds a rich web of connections that shows data flow, dependencies, and inheritance hierarchies. This graph is the agent's brain; it’s the central source of truth for how your entire application is constructed.

Applying Knowledge for Complex Tasks

With this comprehensive graph, the AI can provide very powerful assistance. Its suggestions are no longer just guesses based on the words in your current file. They are informed by the context of the whole project.

For instance, if you start typing the name of a function that exists in another file but haven't imported it yet, the AI can do two things. First, it can autocomplete the function's name. Second, it can automatically add the required import statement at the top of your file. This saves you from having to stop, find the correct file path, and type the import line manually.

This same knowledge allows for large-scale code changes, known as refactoring. If you decide to rename a function that is used in 50 different places across 10 files, the AI consults its graph to find every single one of those locations. With a single command, it can perform the update everywhere accurately, a task that would be tedious and prone to error for a person to do by hand.

Working in Real-Time

This analysis isn't a one-time event that only happens when you first open a project. Modern AI agents perform this work continuously in the background. They monitor the file system for any changes. The moment you save a file, the agent's parser re-analyzes it, updates its AST, and modifies the larger code graph accordingly.

This continuous, real-time updating is what keeps the AI's suggestions relevant. It always has a fresh and accurate model of your code as you write it. This dynamic nature allows it to catch potential errors as you type, suggest refactoring opportunities on the fly, and always provide completions that reflect the most current state of your codebase.