How AI Plans Code Before Writing It

Why Planning Matters

Code that is written without understanding the existing project almost always needs to be rewritten. When a developer jumps straight into writing code without reading the relevant parts of the codebase first, they miss conventions, duplicate functionality that already exists, or break assumptions that other parts of the code depend on. The same is true for AI.

A coding agent that plans before writing produces code that fits naturally into the existing project. It follows the same naming conventions, uses the same patterns for similar operations, and avoids introducing inconsistencies. The planning step takes additional time upfront but saves significantly more time by reducing the number of issues discovered during review.

Step 1: Reading the Codebase

The planning process starts with reading. The agent examines the project structure to understand how files are organized, what frameworks and libraries are in use, and how different parts of the application connect to each other. It reads the files most relevant to the task, paying attention to patterns that indicate how the project expects new code to be structured.

This is more than scanning file names. The agent reads actual code to understand how the project handles things like error handling, logging, database access, and configuration. If every existing database query uses a specific pattern, the agent learns that pattern and uses it in new code. If the project has a particular way of structuring API endpoints, the agent follows that structure.

Step 2: Task Decomposition

After understanding the codebase, the agent breaks the requested task into smaller, concrete steps. A request like "add user authentication" becomes a sequence of specific actions: create the user model, set up the database table, build the registration endpoint, build the login endpoint, add session management, protect existing routes, and update the frontend to show login state.

Each step is small enough to implement and verify independently. This decomposition makes the overall task manageable and creates natural checkpoints where the agent can verify that each piece works before moving on to the next one.

Step 3: Identifying Files and Dependencies

The agent maps out which existing files need to change and what new files need to be created. It traces dependencies to understand what the changes might affect. If adding a new field to a database model, the agent identifies every place that model is used: API endpoints, templates, validation logic, tests, and documentation.

This dependency analysis prevents the common problem of making a change in one place and breaking something in another. By identifying all affected files upfront, the agent can make coordinated changes across the entire project rather than fixing one file and leaving others inconsistent.

Step 4: Considering Edge Cases

Before writing implementation code, the agent considers what could go wrong. What happens if the user submits invalid input? What if the database connection fails? What if two requests try to modify the same data simultaneously? What if a required field is missing?

Experienced developers naturally think about edge cases while planning. They have seen enough bugs to anticipate common failure modes. A well-designed coding agent does the same thing, drawing on patterns from training data to identify the scenarios that are most likely to cause problems. The edge case handling that gets built into the code from the start is significantly better than edge cases patched in after bugs are discovered.

Step 5: Choosing an Approach

For most tasks, there are multiple valid approaches. The agent evaluates the options based on the existing codebase, the complexity of each approach, and the specific requirements of the task. If the project already has a pattern for similar functionality, the agent follows that pattern for consistency. If the task requires something new, the agent chooses the approach that best fits the project's overall architecture.

This decision-making process is informed by the codebase analysis from Step 1. The agent does not pick the approach it is most familiar with; it picks the approach that fits best with the code that already exists. A PHP project with a specific framework gets an implementation that follows that framework's conventions. A Python project with a different structure gets a different but equally appropriate implementation.

How Planning Improves Quality

The difference between planned code and unplanned code shows up immediately in review. Planned code follows existing conventions, handles edge cases, makes coordinated changes across files, and fits naturally into the project. Unplanned code might work in isolation but creates friction when it meets the rest of the codebase.

Planning also makes the review step more effective. When the agent has a clear plan, the reviewer can check whether the implementation matches the plan, whether the plan was appropriate for the task, and whether any edge cases were missed. Without a plan, the reviewer has to reconstruct the agent's reasoning from the code alone, which is much harder.