AI-written code needs end-to-end tests more than ever

Code assistants have changed how fast software gets written. A feature that took a week now takes an afternoon, and a refactor that nobody wanted to touch is one prompt away. The output is often good. The risk is that it is accepted quickly, in larger batches, by people who did not write every line.

Unit tests help, but they check functions, not journeys. A pricing function can return the right number while the checkout button no longer renders, or renders behind a cookie banner. Only a test that drives a real browser through the journey sees what your customer sees.

Why most teams have too few end-to-end tests

End-to-end suites are expensive to build: someone has to learn a framework, find stable selectors, handle waits and test data, and then keep everything working through every redesign. Many teams start, fall behind, and quietly stop running them.

Writing tests the way you describe the product

Qavtrix starts from a sentence: "Sign in, add a jacket to the cart and check out with a promo code." It writes a Playwright test with named steps you can read and edit, runs it in an isolated browser, and keeps the screenshots, logs and trace of every run.

Because the result is ordinary Playwright code, nothing is hidden. Engineers can review it like any other code, and product or QA can describe the next journey without learning the framework.

Keeping humans in charge of what passes

AI is fast at writing and repairing tests, but it should not decide what counts as working. Qavtrix explains failures with evidence and a confidence score, and when the UI changes it proposes the fix and waits for a person to apply it. A failing test is never quietly turned green.