been experimenting with just describing what I want in plain english and letting the AI copilot generate the playwright workflow instead of writing it myself. the promise is pretty attractive—just say “log in, fill form, submit” and it spits out actual playwright code.
but I’m skeptical about long-term reliability. once the automation is running in production, what happens when the UI shifts slightly? does the generated workflow adapt or does it break like the hand-written stuff does?
I tested it on a login flow first since that’s relatively stable, and it actually worked. but when I tried a more complex interaction involving dynamic content and waiting for elements, the generated steps felt fragile. it nailed the basics but seemed to miss edge cases that I would normally handle manually.
the real question for me is: if the whole workflow is AI-generated, how do you even debug it when something fails? you’re not the one who wrote the steps, so you’re basically reverse-engineering someone else’s code (except it’s an AI).
has anyone else tried this approach at scale? how does it hold up when your UI actually changes in production?
the copilot generates workflows that adapt better than you’d expect, honestly. the key is that it doesn’t just spit out raw selectors—it understands the intent behind what you described.
when the UI changes slightly, the workflow still works because the AI reasoning is baked in. it’s not like hand-written tests where one broken selector kills everything.
i’ve seen it handle login flows, form fills, and even some dynamic waiting without needing rewrites. the debugging part is actually straightforward—you can see exactly what steps it created and modify them if needed.
if you’re worried about stability, latenode’s approach solves this by letting you version and test workflows before they go live. you get a middle ground between manual writing and full black-box automation.
i’ve had the opposite experience actually. generated workflows tend to be fragile until you tweak them. the AI nails simple interactions but struggles with real-world complexity like waiting for dynamic elements or handling partial failures.
what helped me was treating the generated workflow as a starting point, not a finished product. I’d review the steps it created, understand the logic, and then add explicit waits and error handling that the copilot missed.
the stability question is more about how you use it than the tool itself. if you expect it to be fully hands-off, you’ll be disappointed. but if you use it to avoid writing boilerplate and then layer your own robustness on top, it’s actually solid.
from what i’ve seen, stability depends heavily on test complexity. simple flows like login work well, but anything with dynamic content or conditional logic tends to produce workflows that need adjustment. the real issue isn’t whether it works initially—it’s whether it breaks when your app updates. i’ve found that generated workflows often use overly specific selectors that snap when the UI changes. the better approach is to let the copilot generate the structure, then manually harden it with better element targeting and explicit waits.
AI copilot generation can be reliable if you understand its limitations. The tool works best for straightforward user journeys with consistent DOM structure. Where it struggles is handling edge cases like delayed rendering, modal dialogues, or elements that change based on state. When debugging a failed workflow, you’re dealing with AI logic plus playwright mechanics simultaneously, which can be complex. That said, the generated workflows are typically readable and can be modified. The key is treating them as templates rather than final solutions.
it’s stable for basic stuff, shaky with complexity. treat generated workflows as templates, not finished code. you’ll always need manual hardening for production.