How do I check dependencies in AI-generated code before launch?

Review an AI-suggested dependency the same way you would review one proposed by a developer: verify its identity, explain why it is needed, and assess its effect on the application. Inspect what changed in the dependency tree and build process before approving installation or release.

GitHub's dependency-review documentation describes reviewing dependency changes in pull requests. Such tooling can support the review, while the team still needs to judge whether the proposed package belongs in the project.

Which questions should a reviewer answer?

Start with the exact package and source. Check that the name, publisher, repository, and documentation correspond to the intended project. A familiar-looking name is not sufficient evidence of identity.

Then ask why the application needs it. A small formatting task may not justify adding a large framework or another networked service. Existing platform features or a dependency already in use might solve the problem with less operating complexity.

Finally, identify where the package runs: in a developer's tools, during the build, on the server, or in the customer's browser. Those positions create different access and maintenance considerations.

What does the dependency add beyond its advertised feature?

The answers should be recorded with the change. They make future updates easier to assess and help a new maintainer understand why the package exists.

How should we handle vulnerability findings?

Look at the affected version and usage path rather than treating every scanner result as identical. A finding needs a responsible owner who can assess its relevance, proposed fix, and release consequence.

Avoid automatically upgrading every dependency to the newest version as a response. An update can change behaviour or break compatibility. Review the change and run the tests needed for the workflows it affects.

An illustrative scheduling app might add a date library to support time zones. The review should check its behaviour for the actual booking rules, including daylight-saving transitions where relevant. A successful install does not establish that those business cases work.

What should the AI coding workflow do differently?

Ask the agent to explain new dependencies before adding them. Keep changes small enough that the reviewer can inspect both the source changes and the lockfile. If the agent cannot justify a package or identify its source, pause that change and investigate.

Use a reproducible installation process for releases. Record the runtime and tooling versions the project requires, and keep generated dependency files under the project's agreed change process.

A business owner does not need to evaluate every package personally. The practical requirement is a named engineering owner, a review process, and evidence that dependency changes receive more attention than clicking "accept all."

Get control of an unfamiliar dependency tree

Bring the repository, current deployment, and any scan findings. Request an AI-built app review to understand which dependency issues matter and how to address them without disrupting working features.

Frequently asked questions

Is a popular package automatically safe?

Popularity is one signal, not a guarantee. Verify identity, relevant risks, maintenance, and whether the version and usage fit your project.

Can a coding agent update dependencies for us?

It can prepare a change, but someone must review compatibility and verify the application. Automated preparation does not remove release responsibility.

Should we remove every package we do not recognize?

First identify why it is present and what depends on it. Some are indirect dependencies required by a legitimate component. Make removal and replacement decisions with that context.