Using an AI Coding Agent to Update Dependencies

Dependency updates are one of those software tasks that sound simple until you actually do them.

Updating a package version can sometimes be a one-line change.

Then the project stops building.

An API changed. A configuration option was removed. A transitive dependency conflicts with something else. A test starts failing three directories away from the package you updated.

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

Instead of just changing a version number, you can give the agent the task of updating a dependency and let it inspect what the project actually relies on.

A typical workflow can look like:

Check the current dependency → update it → inspect the affected code → run the project or tests → find what broke → make the required changes → test again

That's the kind of workflow we're building Clixad around.

Clixad is a cloud-based AI coding agent that runs in the terminal. It can inspect a project, read files, modify code, run commands, execute tests and iterate when a change causes problems.

For example, a task could be:

Update this project to the latest version of the package and fix anything that breaks.

The useful part isn't just changing package.json.

The agent can inspect how the dependency is being used, identify related code, run the relevant commands and use the resulting errors to continue working.

This can be useful for:

updating outdated packages
migrating to a new major version
fixing breaking API changes
updating frameworks and libraries
resolving dependency-related build errors
checking whether existing tests still pass

It also changes the role of AI coding from code generation to maintenance.

A chatbot can tell you that a package has a breaking change.

An agent can actually work through the project after the change and verify what happened.

Clixad runs the AI models in the cloud, so users don't need to run a model locally or provide their 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, so smaller maintenance tasks can be handled without spending credits while the free allowance is available.

For me, this is one of the more practical uses of AI coding agents.

New features are exciting, but keeping an existing project working is where a lot of development time actually goes.

Dependency updates are a good example of a task that benefits from an agent that can change code, execute the project and react to what happens next.