Giving AI Coding Agents Context Without Giving Them Your Entire Codebase

AI coding agents are proving quite adept at understanding existing codebases given sufficient context. However, there is a caveat: not all context is beneficial. While it might seem logical to provide an agent with the entire codebase, doing so often introduces unnecessary noise, consumes more computational resources, reveals irrelevant implementation details, and makes it harder to pinpoint what truly matters.

As you become more accustomed to working with AI coding agents, it becomes clear that a more targeted approach to context is preferable.

The temptation is to give these agents everything - the complete repository, all configuration files, database schemas, API endpoints, environment variables, authentication logic, old implementations, unrelated features, and even internal business logic. The rationale is that more knowledge leads to better decisions. Yet, software engineers typically don't operate this way.

When joining an unfamiliar project, you're not handed the entire repository and told to figure it out. Instead, you receive a specific task, some background information, relevant files, and necessary conventions to grasp the issue at hand. AI agents can follow a similar process.

To begin, clarify the specific task the agent needs to accomplish. For instance, if the goal is to add a contributor submission feature, the agent probably doesn't require access to your entire admin dashboard, payment system, analytics implementation, or every unrelated database table. What it does need includes: the contributor submission requirements, the existing article model, the submission API/server action, authentication and session interface, relevant UI components, and validation conventions.

This targeted context establishes a useful working boundary without overwhelming the agent with extraneous information.

One effective method to reduce unnecessary context is to differentiate between a system's functionality and its implementation. For instance, the agent may need to know that your application has an authentication system, as seen in this snippet:

```

const session = await getSession();

if (!session?.user) {

return unauthorized();

}

```

However, it may not need to understand the entire implementation, including session storage, OAuth providers, cookie configuration, database adapters, token handling, and internal authentication utilities. The agent only requires the interface to utilize these components. Unless the task involves modifying authentication, exposing all these details can add complexity without enhancing the solution.

Another useful perspective is to consider your application as a series of boundaries. When requesting an agent to modify a UI component, it may only need the UI layer and relevant interfaces from the underlying layers. If you're altering database behavior, you'll likely need more of the data layer. The key point is that the agent's working boundary can be smaller than the application's boundary.

Runtime boundaries versus agent boundaries is another aspect to consider. Your application might have access to authentication, database, payment systems, users, analytics, and admin functionalities. Yet, an agent working on a profile component might only require the profile component, user type, profile API contract, validation schema, and relevant UI conventions.

The entire system is needed for the application to function, but the agent only requires enough of the system to complete the task. This distinction becomes increasingly significant as agents gain more autonomy.

Creating sanitized blueprints can also be beneficial. For example, you can describe a feature like contributor posts as follows:

Feature: Contributor Posts

UI

└── ContributorPostForm

Server

├── submitContributorPost()

└── validateContributorPost()

Data

├── contributor_posts

└── users

Authentication

└── getCurrentUser()

This architectural blueprint outlines the system without exposing every implementation detail. The agent can grasp the architecture from this blueprint without reading every file involved in the system. You can then provide the actual implementation files when they are required. Moreover, this blueprint serves as useful documentation for both agents and human collaborators.

Context can also be a security boundary. An AI coding agent that has access to everything could potentially access sensitive business logic, internal APIs, customer information, database structures, private documentation, and even infrastructure configuration and secrets if your environment is not adequately secured. While an agent might still need access to certain areas of the system, a more deliberate architecture can limit its access to only what is necessary for the task at hand.

A practical approach for an AI agent might involve a workflow that gradually defines the context needed for each step of the task, rather than providing a single, expansive prompt.