I’ve been experimenting with describing what I need in plain English and seeing if it actually generates usable browser automation workflows. The idea sounds amazing in theory—just tell the system what you want and get back ready-to-run code.
I tried a few workflows last week. Simple ones like “navigate to a site and fill out a form” worked surprisingly well. But when I got into anything with conditional logic or dynamic selectors, things got messy. The generated workflow would work the first time, then fail when the page structure changed even slightly.
The real friction point seems to be that plain language descriptions miss all the edge cases. You might say “extract the product price” but what happens if the price isn’t there? What if it’s in a weird format? The AI-generated workflow just assumes the happy path.
I’ve also noticed that the generated code tends to be pretty verbose. It works, but it’s not optimized. Feels like something that gets the job done but needs tweaking to run reliably in production.
Has anyone else hit these walls, or am I just not being descriptive enough in my plain text instructions?
You’re actually describing exactly where the AI Copilot shines once you understand how to work with it. The key is that plain language generation is your starting point, not your end product.
What I do is generate the workflow first, then look at what the AI created. I treat it like a template. Add your error handling, specify exact selectors, build in fallbacks for missing data. The copilot saves you from writing the boilerplate, but you still own the logic.
The thing that changed everything for me was iterating on the generated workflow. Let the copilot do the heavy lifting, but validate each step. For dynamic selectors, I’ll add screenshot capture right before the extraction step so I can debug visually.
Also, the AI can be more precise if you describe edge cases in your initial prompt. Something like “extract price, but if not found return null” gets better results than just “get the price.”
This workflow building approach works way better when you’re using Latenode because you can tweak and test in the same interface without leaving the platform.
Plain English to working automation is genuinely useful but it’s a starting point, not the finish line. I’ve had the same experience—basic stuff converts fine, complex logic gets weird.
What helped me was understanding that the generated workflow is like a rough draft. I treat it as a scaffold and then actually think through what could go wrong. Add your own validation steps, handle missing data explicitly, test with different page states.
One thing I started doing is breaking complex tasks into smaller workflows. Instead of one massive “do everything” prompt, I describe specific chunks. Like one workflow just for navigation, another just for extraction. Easier to debug and the AI generates more predictable code that way.
The issue you’re running into is that natural language lacks precision. When you say “extract the price,” you’re being vague even though it seems clear to you. The generated automation can’t anticipate edge cases you didn’t explicitly mention.
I’ve found that more detailed descriptions yield better results. Specify HTML structure, explain what happens if data is missing, describe the page layout. The more context you provide, the more robust the generated workflow becomes. It’s still not perfect, but it handles real-world messiness better.
Your experience reflects the current state of AI-assisted workflow generation. The technology excels at routine conversions but struggles with conditional branching and data validation. The workflows are functional scaffolding, requiring refinement for production use.
The practical approach is post-generation iteration. Review the generated steps, add explicit error handling, test against variations in the target webpage. This turns the AI output from a first attempt into a reliable automation by incorporating your domain knowledge about where failures occur.
generated workflows need tweaking for real use. edge cases arent handled automatically. break tasks into smaller workflows and test each part separately. that approach works beter than trying one massive description.