I’ve been wrestling with headless browser automation for a while now, and the manual setup is killing me. Writing scripts from scratch for scraping or form submission feels like reinventing the wheel every time. I keep hearing about AI Copilot Workflow Generation, and I’m curious if it actually works in practice. The pitch sounds good—just describe what you need and get a ready-to-run workflow—but I’m skeptical about how well it handles real-world sites with their messy HTML and dynamic content. Does anyone here use it? How stable are the generated workflows, and do they usually work on the first try, or do you end up tweaking them anyway? I’m especially interested in whether it handles authentication flows or just simple data extraction.
I’ve used it on several projects, and honestly it’s been solid. The key is being specific about what you’re trying to extract or how the form works. I describe the target site, what data matters, and any tricky bits like login steps. The AI generates a workflow that usually works without much tweaking.
The real win is time. Instead of writing Playwright or Puppeteer from scratch, you’re up and running in minutes. For scraping product data or submitting forms at scale, it’s cut my setup time down significantly.
Yeah, sometimes you need to adjust selectors if a site layout changes, but that’s expected. The workflow structure itself stays solid. Give it a shot and see how it meshes with your use case—I think you’ll be surprised.
I’ve tried it for a few different scraping projects, and the results were surprisingly good. The thing I noticed is that it works best when you’re clear about the expected output. If you give it vague instructions, the workflow is vague too.
One project I did was extracting pricing data from several e-commerce sites. The generated workflow handled the pagination and data extraction without much intervention. I had to adjust a few CSS selectors, but nothing major.
The authentication part works if you’re upfront about how the login happens—form submission, token-based, that kind of thing. Overall, I’d say it’s worth testing on a smaller task first to build confidence before deploying it on something critical.
I started using it last year for form automation, and it’s been reliable more often than not. The generated workflows tend to be pretty clean. What matters most is how well you describe the task. I’ve found that including example URLs and clear descriptions of the target elements gets you closer to a working solution faster. The error handling is decent too—it usually catches missing elements gracefully rather than crashing. My advice is to test it on a simple task first to understand how it interprets your instructions, then scale up.
The technology behind it is solid. From a technical standpoint, the workflow generation uses pattern recognition to map your description to common headless browser patterns. I’ve analyzed several generated workflows, and they’re well-structured. The async handling is competent, and it properly manages browser contexts. Success rate depends heavily on clarity of input. Clean descriptions yield reliable outputs. I’d estimate 75-80% of first-run success on well-scoped tasks.
Yes, works if you describe precisely. Start small to validate.
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