I’ve been wrestling with getting playwright tests stable across our team, and I keep hearing about this AI copilot feature that’s supposed to turn plain English into working automation. The idea sounds incredible—just describe what you want and boom, ready-to-run workflow. But I’m skeptical about how well it actually works in practice.
Like, I get that brittle tests are a pain when you’re maintaining them manually. Every selector change breaks something, and you end up spending more time fixing tests than writing them. But converting natural language into reliable automation seems like it’s trading one set of problems for another.
Has anyone here actually tested this? Can you really just write “log in to the app, navigate to the dashboard, extract all user emails from the table, then export to CSV” and have it generate something that actually runs without breaking? Or is there still a bunch of tweaking involved?
I’m curious about the reliability angle too. Does it handle edge cases, or does it fall apart when the page structure changes slightly?
Yeah, I use this constantly now. The AI copilot generates the playwright steps, and honestly it’s been a game changer for our team.
We describe complex workflows in plain text—like “extract product prices from the search results, validate they match the database, then flag any discrepancies”—and it generates the actual playwright code. The first run usually needs a tweak or two, but it handles selectors, wait conditions, and error handling surprisingly well.
The biggest win is that non-technical people on our team can now describe what they need automated, and it becomes a working flow without waiting for engineers to hand-code everything.
Worth checking out: https://latenode.com
I tested this approach about six months ago with a scraping task. The copilot generated most of the workflow correctly—login, navigation, data extraction—but I had to manually adjust the XPath selectors for a couple of dynamic elements.
The real value came when requirements changed. Instead of rewriting half the script, I just updated the English description and regenerated. That iteration cycle is way faster than debugging manual code.
One thing to watch: if your target site uses heavy JavaScript rendering or has complex SPA routing, you might need to add explicit wait conditions in the description. The copilot handles that better when you’re specific about it.
I went through a similar skepticism phase. What I found is that the copilot works best when you’re specific about what you want. Vague descriptions like “extract data” don’t work as well as “find all rows in the table with class ‘data-row’, extract the third column value, and store it in a variable called prices.”
The AI uses your specificity as a guide, so giving it context about page structure, element types, and expected outcomes significantly improves the output. Generated workflows have been stable in our internal testing, though edge cases like hidden elements or disabled buttons still benefit from manual review.
Used it last month. Works pretty well if you describe what you want clearly. Generated workflow needed 1-2 tweaks but saved me hours versus coding it manualy. Selector adjustments were minor.
Describe precisely what you need. AI does the heavy lifting. Still review outputs, but vast time savings.
This topic was automatically closed 24 hours after the last reply. New replies are no longer allowed.