Using an AI Coding Agent With an Existing Codebase

AI coding is often presented as a way to build a new app from scratch.

But one of the more useful applications is working on code that already exists.

Most real projects aren't empty folders. They already have components, dependencies, configuration, APIs, tests, and plenty of code that you didn't write yourself.

That's where an AI coding agent can be useful.

Instead of asking an AI to generate a piece of code in isolation, you can give the agent a task and let it first understand how the project is structured.

A typical workflow might look like this:

Inspect the codebase → find the relevant files → understand the existing implementation → make the changes → run commands or tests → fix anything that breaks

This is different from simply asking a chatbot for a code snippet.

We've built Clixad around this kind of workflow.

Clixad is a cloud-based AI coding agent that runs in the terminal. It can read an existing project, modify files, execute commands, test changes, debug errors and continue iterating on the task.

For example, instead of asking:

"How do I add authentication to a React app?"

you can give an agent an existing project and ask it to add authentication to that project.

The agent can then inspect the relevant files and work with the code that's already there instead of giving you a generic implementation that you have to adapt manually.

This is particularly useful for:

adding features to an existing SaaS
fixing bugs across multiple files
refactoring old code
updating dependencies
writing or fixing tests
understanding an unfamiliar codebase
continuing a project that was started with AI

The cloud aspect also matters.

With Clixad, the AI models run remotely, so you don't need to run a coding model locally or provide your own API key. There is also no mandatory monthly subscription.

Users get free credits, with additional credits available through advertiser-funded offers, forms and surveys.

We've also made the five cheapest models free within their daily limits, which gives users a way to work on smaller tasks without spending credits while they're within those limits.

For new projects, AI can help generate the first version.

For existing projects, the more interesting question is whether an agent can understand what is already there and safely work within it.

That's where we think agentic coding becomes much more useful than simple code generation.