I’ve been experimenting with converting plain English test descriptions straight into Playwright workflows, and I’m curious if anyone else has tried this seriously. The pitch sounds great—just describe what you want and get a ready-to-run workflow—but I’m wondering about real-world stability.
My concern is that UI changes often break automations in ways that are hard to predict. If I describe “log in and submit a form,” does the generated code actually adapt when the website redesigns, or do I end up babysitting it constantly?
I’ve seen a few attempts at this, but most seem to handle the happy path well and then explode on edge cases or dynamic content. Has anyone actually built something that stays stable over time without constant tweaking? What’s your actual success rate with AI-generated workflows versus hand-coded ones?
I’ve been doing this for a while now, and the key is not just generating the code—it’s how the workflow adapts when things change. Plain English descriptions work well for the initial generate, but you need a system that can learn from failures and adjust.
With Latenode, the AI doesn’t just generate once and ghost you. The workflow can be connected to real error handling, and you can set up logic that catches when selectors break and tries alternatives. I’ve used this for production workflows, and the stability comes from chaining the right models and adding feedback loops.
The plain English part is just the starting point. You layer in validation, error handling, and some AI agents that watch for failures and adjust. That’s where it actually becomes stable.
I ran into the exact same problem. Generated workflows worked great on the first run, then broke within a week when the site got updated.
What actually helped was treating the generated workflow as a skeleton, not a finished product. I started layering in explicit wait conditions and fallback selectors. The difference was night and day.
Also, I found that describing the workflow in terms of what should happen, not how to find elements, gives you better output. Instead of “click the blue button on the right,” say “submit the login form.” The generated code ends up more flexible because it’s working from intent, not specific coordinates.
The problem with AI-generated workflows is that they inherit the brittleness of the patterns they’re trained on. Most generated code relies on CSS selectors that break instantly when styling changes. I tested this across multiple websites, and the failure rate was around 40% within the first month of deployment.
What improved stability was adding intermediate validation steps. After each action, check that the expected element is present. This adds overhead but catches breakage early. The generated workflow itself was about 60% reliable, but with validation layers it climbed to 85%+.
Plain English descriptions can generate functional workflows, but stability depends heavily on how you structure error handling. I’ve found that the AI does better with semantic descriptions rather than UI-specific ones. The generated code tends to be defensive when trained on well-documented test patterns.
One approach that worked well was generating multiple selector strategies upfront. Instead of relying on a single CSS selector, have the workflow try XPath, then data attributes, then fallback text matching. This kind of resilience needs to be baked into the generation logic, not added after.