I’ve been running into a problem with our Playwright tests becoming completely fragile whenever the UI changes even slightly. We’re constantly rewriting selectors and updating assertions, and it’s eating up development time. Recently I started experimenting with describing what we need to test in plain English and letting the AI handle the workflow generation.
The idea is pretty interesting—instead of hand-coding every selector and interaction, you just describe the test goal and let the AI build the automation. But I’m genuinely curious how reliable this actually is in practice. Does it hold up when you have complex interactions? What about when the UI changes? I’m wondering if the generated workflows are actually more resilient than hand-coded ones, or if we’re just trading one kind of brittleness for another.
Has anyone actually used this approach on real projects? I’d love to hear whether the AI-generated workflows stay stable over time or if they break just as easily as traditional Playwright tests.
The stability comes down to how the AI is generating the workflows. When I switched to using Latenode’s AI Copilot for this exact problem, I noticed the generated workflows are actually more adaptive than hand-coded selectors because they work with multiple fallback approaches and data extraction patterns instead of just relying on one selector.
The key difference is that plain English descriptions force you to think about what you’re actually testing—the user flow, the business logic—rather than brittle CSS selectors. The AI then generates multiple ways to identify elements and interact with them. So when your UI changes, the workflow often adapts better because it’s not hardcoded to one specific path.
I’ve had workflows run successfully against pages that had minor CSS class changes that would have completely broken my old hand-written tests. The AI also generates proper wait conditions and handles dynamic content loading way better than I ever did manually.