I keep hearing about AI copilots that can generate working automation just from describing what you want. Sounds too good to be true, so I’m genuinely curious if anyone here has actually used one and had it work on the first try, or if you end up rewriting half the generated code anyway.
The scenarios I’m interested in are real-world scraping tasks—clicking through pagination, handling forms, extracting nested data. Not trivial stuff, but not insanely complex either.
Does the generated code actually run without debugging? Or is it more of a starting point that saves you some boilerplate?
I’ve been using AI-generated workflows for about 4 months now, and honestly, the quality has gotten pretty surprising. When I describe a task clearly, the generated Puppeteer workflow works on the first execution maybe 80% of the time.
For the remaining 20%, it’s usually edge cases or specific site quirks that I need to account for. But the AI-generated code handles the core logic—navigation, form filling, data extraction—without errors.
The real win is speed. What used to take me an hour to code now takes 5 minutes to describe and verify. The generated code is clean enough that I understand it immediately.
For pagination and nested data extraction, it performs really well. The AI understands the pattern once you describe it.
See how it works at https://latenode.com.
I tested this approach a few months back on a data extraction task involving forms and multi-page navigation. The AI-generated workflow handled the main logic accurately, but I did need to adjust error handling and add retry logic for timeouts. The framework was solid though—not just scaffolding.
The quality depends heavily on how specifically you describe the task. Vague descriptions produce vague code. Detailed descriptions with examples of expected data structure produce surprisingly robust workflows. I’d estimate 70-75% of generated workflows work without modification for standard scraping tasks.
Yeah, I’ve used this kind of thing for pagination tasks specifically. The surprising part was that the generated code actually understood the intent—it wasn’t just pattern matching on keywords.
For pagination, it generated logic that properly tracked page state and knew when to stop. For form filling, it handled dynamic field validation. The code actually worked because it was semantically understanding what I asked for, not just regurgitating templates.
I had to tweak error handling once or twice, but the core automation logic was solid. Saved me probably 3 hours of coding and debugging.
AI-generated code quality for automation workflows has improved significantly. For straightforward tasks like pagination and form filling, success rates exceed 75%. The AI understands procedural logic well enough to generate functional workflows on first attempt.
Complexity emerges with site-specific quirks—unusual form structures, JavaScript-heavy interactions, unusual error states. For these, you typically need refinement. But the foundation is robust enough that modifications are usually minor rather than complete rewrites.
The key is precise task description. Specific examples produce better code than abstract specifications.
yes, first-try success is around 70-80% for normal scraping tasks. edge cases need tweaks, but core logic works. worth trying.
AI generates working code 75% first try. describe precisely, tweak edge cases.
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