PR Copilot vs AI Test Suites: Where to Start
Both products run on the same DebuggAI account, the same underlying test execution platform, and the same idea, that you shouldn't have to hand-write and maintain a test suite to get real coverage. But they solve different problems, and teams evaluating DebuggAI for the first time usually want a straight answer on which to set up first.
PR Copilot, in one paragraph
Install a GitHub App, and every push gets analyzed for what changed, a targeted Playwright test plan gets generated and run against a live, isolated build of your branch, and the result, pass or fail, with screenshots and video, lands as a comment on the pull request. No config file, no code changes, about two minutes to set up. See the full setup guide.
AI Test Suites, in one paragraph
Add a lightweight SDK to your app, use it locally the way a real user would, and DebuggAI turns the function calls, parameters, and error states it observes into test cases in your existing framework, Jest, Pytest, JUnit, and others. The output lives in your repo as tests you own, generated from real behavior rather than imagined up front. See the full setup guide.
Side by side
| PR Copilot | AI Test Suites | |
|---|---|---|
| Trigger | Every push or pull request | You running the app locally with the SDK attached |
| What it tests | The browser layer: real user flows in a real browser | Function-level logic, parameters, and error states |
| Output | Pass/fail, screenshots, video, posted to the PR | Test cases in your existing test framework |
| Setup effort | Install a GitHub App, no config | Add an SDK, use your app |
| Best for | Catching UI regressions before merge | Building a lasting regression suite from real usage |
| Blind spot | Won't exercise logic a browser flow never touches | Won't catch a UI regression that doesn't change behavior a script would notice |
The honest answer
Start with PR Copilot. It requires the least setup, gives you signal on your very next pull request, and catches the class of bug that's most expensive to find late: something that looks fine in the code but breaks the moment a real browser clicks through it.
Add AI Test Suites once you've got flows worth turning into a durable, framework-native regression suite, particularly backend logic, validation, and error handling that a UI click-through won't exercise on its own.
Can you run both?
Yes, and most teams that use DebuggAI seriously end up doing exactly that. They're not competing approaches to the same problem, they cover different layers of the same application. PR Copilot watches what changed and what a user would see; AI Test Suites captures what your code actually does when it's used. Run them together and the gap between "CI passed" and "it actually works" gets a lot smaller.