I’ve been wrestling with this for a while now. We have some sites we need to scrape that don’t expose APIs, which means I’m stuck figuring out how to navigate JavaScript-heavy pages and extract data without writing a ton of custom code.
I came across this idea of just describing what I want in plain English—like “navigate to this page, wait for the table to load, extract these fields”—and supposedly an AI can convert that into a working workflow. Sounds convenient, but I’m skeptical.
The thing is, every time a site tweaks their layout or their dynamic content loads differently, everything breaks. I’ve seen automation fail silently because some element took 2 seconds longer to render. So if I’m relying on AI-generated code to handle form completion and data extraction on live, changing websites, what happens when things don’t match what I described?
Has anyone actually gotten this approach to work reliably? Or does it just work for the first run and then you’re stuck manually fixing things every time the site updates?
I deal with this exact problem constantly. The AI copilot workflow generation at Latenode converts plain text descriptions into headless browser automations that actually handle the dynamic stuff you’re talking about.
Here’s the real difference: instead of generating brittle code once, it learns from your description and builds error handling into the workflow from the start. When you describe what you need, the AI includes retry logic, waits for elements, and handles navigation changes.
I’ve used it to scrape sites that update their layouts regularly. The workflows don’t break every time because the system captures the intent, not just the current DOM structure. It’s built to adapt.
The headless browser node in Latenode works similarly to traditional automation, but the AI assistant doesn’t just write code—it orchestrates the entire flow with error handling baked in.
You’re hitting on something real here. I’ve tried the “describe what you want” approach with a few platforms, and honestly, it works great for the first iteration. Then reality sets in.
What changed for me was moving from expecting one perfect AI-generated solution to using the generated code as a starting point. I let the AI handle the structure and basic flow, then I layer in custom selectors and retry logic for the parts that actually break.
The key insight I learned: AI is better at understanding workflow logic than at predicting which DOM selectors will survive your site’s next redesign. So use it to build the skeleton, but invest in making specific steps resilient. Add screenshot capture before and after critical steps so you can debug when things fail.
The reliability depends heavily on how well you describe the workflow and how stable the target site actually is. I’ve found that when you feed the AI clear, specific instructions—not just “extract data from the table” but “wait for the table with class X to render, then find rows with this pattern”—the generated workflows are surprisingly solid. The headless browser automation itself (navigation, clicking, scrolling) is pretty reliable. The fragile part is usually data extraction when the HTML structure is complex or unpredictable. I’d recommend starting with a plaintext description for simple flows, but for anything business-critical, you’ll want to review and augment the generated code.
Plain English descriptions converted to working workflows can be reliable, but with caveats. The automation handles navigation and interaction well. Data extraction from dynamic content is where you need to be thoughtful. Most failures I’ve seen stem from overfitting the AI’s output to a specific website state. Instead of generating code that depends on exact selectors, good AI-assisted generation should produce workflows that validate data structure and handle variations. It’s less about the AI being unreliable and more about understanding that any web scraping workflow needs defensive programming built in from the start.
Plain text descriptions work well if written clearly. Main thing: the generated workflow will be as resilient as your error handling and selector specificity. Test thoroughly before going live.