I’ve been curious about this for a while now. The pitch sounds amazing—just describe what you want in plain English and get a ready-to-run workflow. But I’m skeptical about how well it actually works in practice.
Like, I’ve tried other AI-powered tools before and they always need some babysitting. The generated code is usually 70% there, which means I’m spending half my time fixing edge cases and handling things the AI missed.
For browser automation specifically, there are so many variables. Dynamic content, timing issues, selectors that change, login flows that are unpredictable. Does the AI copilot actually handle this complexity, or does it just generate basic workflows that work on the happy path?
Has anyone here actually shipped a real automation using this feature without needing to manually adjust it afterward? And if you did need to tweak it, how much rework are we talking about—like 5 minutes or more like an hour of debugging?
I’ve been using this exact feature for data extraction workflows, and honestly it’s way better than similar tools I’ve tested. The copilot generates solid boilerplate that handles most of the repetitive setup.
The key difference is that after it generates the initial workflow, the visual builder lets you see exactly what’s happening at each step. So you’re not staring at generated code trying to figure out what went wrong. You can actually debug visually.
For dynamic content, I’ve found that combining the copilot output with conditional logic in the builder catches most edge cases. Login flows usually need one or two tweaks because they’re site-specific, but that’s just adding a few steps in the builder.
Real example: I described a workflow that needed to extract pricing data from multiple product pages with variable layouts. The copilot generated the scraper structure, I added some conditional steps for layout variations, and it’s been running stable for months.
The time investment is real, but it’s way less than building from scratch. More like 20-30 minutes of builder tweaking instead of hours of coding.
I think the expectation matters here. If you’re expecting zero tweaks, you’ll be disappointed. But if you’re comparing it to writing browser automation from scratch, it’s a different story.
What I’ve learned is that the description quality really affects the output. When I’m vague, the generated workflow is vague. When I spell out the exact steps—click here, wait for this element, extract that data—the copilot nails it.
The other thing is that browser automation is inherently fragile because websites change constantly. No tool, AI or not, will generate something that never breaks. The advantage of using the copilot is that when something does break, you’re not decoding someone else’s code. The visual builder makes it obvious where the failure point is.
My honest take: it shaves off maybe 60-70% of the initial work. The remaining 30-40% is debugging and edge cases. But that’s still way better than starting from zero.
I’ve tested this with a few moderately complex workflows. The ai copilot does genuinely save time on the skeleton of the automation. The output includes sensible step ordering and handles basic waits and element detection reasonably well.
Where I hit friction: when pages have dynamic behavior or when the workflow involves multiple conditional branches. The initial generation tends to be linear and doesn’t anticipate failure scenarios. So yeah, tweaking is necessary, but it’s targeted tweaking rather than reworking the entire thing.
For browser automation specifically, I’d say the copilot gets you to a point where you have maybe 70% of a working solution and need to validate it against real page behavior. If the site is stable and relatively simple, you might get away with minimal adjustments.
The copilot’s effectiveness really depends on how deterministic your target workflow is. For straightforward scraping tasks with stable DOM structures, it produces surprisingly solid output with minimal adjustments needed.
The challenge emerges when dealing with variable page layouts or complex user interactions. The generated workflows lack context about which variations matter and which don’t. You end up adding conditional logic, retry mechanisms, and error handling that the initial generation didn’t anticipate.
From my experience, the copilot excels at eliminating boilerplate and routine setup steps. But the domain knowledge about your specific site or task still needs to come from you during the tweaking phase.