Debugg AI

Teams use Debugg AI during everyday code review to spot UI and flow breakages before anything reaches main. After pushing a commit to a pull request, the app runs through common paths in the product like signing in, navigating key pages, creating or updating records, and completing checkout-style actions when they exist. If something changes unexpectedly, it leaves feedback in the PR so the author can fix it while context is fresh.

A typical workflow is to open a PR, wait for the automated run to finish, then review the attached outcomes alongside the diff. When a failure appears, engineers use the shared evidence from the run to understand what the browser saw and what step caused the issue. That makes it easy to decide whether to adjust code, update a component, or correct a configuration mistake without switching tools.

It also fits well into fast iteration loops. Developers can push incremental commits and let each update trigger another check, confirming that a fix truly resolves the problem and that nearby journeys still work. For teams that don’t want to build and maintain their own end-to-end setup, this approach provides consistent verification on every PR, helps reviewers focus on code quality, and reduces last-minute manual testing before release.