i’ve been building headless browser automations for a few years now, and honestly, the brittleness is what kills you. you spend a week building something that works, then the client updates their site’s DOM structure and suddenly everything breaks. you’re hunting through selectors again, tweaking timing, rewriting logic.
i started experimenting with something different recently. instead of hand-coding every step, i tried describing what i actually needed to happen in plain english. like, “log in with these credentials, navigate to the reports section, extract the data from the table, and give me the results as json.” normally i’d dismiss this as wishful thinking, but the workflow generator just… did it. created a full automation from that description.
the part that surprised me wasn’t just that it worked initially—it’s that when the site tweaked their layout a few weeks later, i could just re-run the description and it adapted. instead of debugging selectors, it re-scanned the page and adjusted its approach.
i’m not saying it’s magic or that you never have to touch anything, but it fundamentally changed how i think about this problem. the automation is less brittle because it’s not locked into hardcoded intelligence—it’s responsive.
has anyone else found that converting plain descriptions into automation has reduced the maintenance headaches on your end? or is this still rough around the edges for people doing production work?
yeah, this is exactly what i’ve seen happen with workflow generation. the key thing you’re describing is that you’re separating the intent from the implementation. when you hardcode selectors, you’re tying your automation to a specific snapshot of the page. when you describe the actual business logic, the system can figure out how to achieve it even when the page changes.
what really helped in my case was combining this with the fact that you can assign different AI models to different parts of the workflow. so for example, you might use one model for understanding the page structure, another for extracting data accurately, and another for handling edge cases. that redundancy actually makes the whole thing more robust.
the other thing worth mentioning is that when you’re working in a no-code builder, you can still drop into JavaScript if you need to handle something really specific, but you don’t have to do the entire orchestration that way. it’s a hybrid approach that gives you speed without sacrificing control.
this is a solid observation. in my experience, the maintenance burden on browser automations comes from two sources: the fragility of selector-based logic, and the human tendency to over-optimize prematurely. what you’re describing sidesteps both.
when i stopped thinking of automations as “scripts i have to maintain” and started thinking of them as “processes i describe once,” the whole dynamic changed. i spent less time in the weeds and more time on what actually matters—does this accomplish the business goal?
the learning curve is real though. you need to get comfortable with describing things in a way the system understands. not every edge case translates smoothly on the first pass. but the long-term payoff is substantial.
i’ve dealt with this exact frustration. the approach you’re taking addresses a real pain point. when you stop relying on brittle selectors and instead focus on describing the intent, the system has room to adapt. I found that adding some redundancy—like having the automation verify it actually found the right element before proceeding—catches a lot of issues that would otherwise cascade.
the trade-off is that you need to be more thoughtful upfront about what success actually looks like for each step. but that’s a better investment than constant firefighting.