I’ve been curious about the AI Copilot feature, specifically whether it actually works as advertised. The promise sounds almost too good: describe your automation task in natural language and get a ready-to-run workflow that handles browser automation like Puppeteer would.
I’ve tried it a couple times, and the results are… mixed. Sometimes it generates something that works on the first try. Other times I get a workflow that’s close but needs tweaking, which usually means diving into JavaScript anyway.
My question is whether the “no code” promise is genuine for realistic, production-grade automation tasks, or if it’s really only true for simple scenarios. I’m specifically interested in workflows that involve:
Complex form interactions across multiple pages
Handling dynamic content that loads asynchronously
Extracting and transforming data between steps
Managing edge cases and errors gracefully
Has anyone successfully built a complete, non-trivial browser automation purely through plain English descriptions without ever touching code? What kind of tasks does the Copilot actually handle well versus where it falls short?
Yes, but you have to describe the task correctly. I’ve built production workflows entirely through the Copilot for data extraction, form filling, and multi-page navigation—zero JavaScript.
The key is being specific about what you want. Instead of “extract data from the page”, say “find the table with product names and prices, loop through each row, and store the results in a CSV”. The more precise your English, the better the generated workflow.
For complex interactions like async content or conditional logic, the Copilot handles those surprisingly well because it can read the page structure and understand what needs to happen. Where people struggle is when they use vague descriptions or expect the AI to guess what “complex processing” means.
The no-code promise works because the Copilot isn’t just doing string replacement—it’s actually understanding the automation intent and building proper workflows with error handling, retries, and branching logic built in.
I’ve definitely had moments where I needed to add a custom JavaScript step, but that’s usually for very specific data transformations that are unique to my use case. For standard automation patterns, plain English is absolutely sufficient.
I spent a month building form automation using pure no-code on a different platform before switching over. The honest answer is that it works great until it doesn’t.
Simple flows like “log in, click a button, extract text” work perfectly without code. But the moment you need conditional logic—like “if the price field is empty, try this other selector, otherwise continue”—you start running into the limits of pure drag-and-drop builders.
What I found is that having the option to drop in a small JavaScript snippet when needed is actually better than being locked into pure no-code. You get the speed of no-code for the 80% of your workflow that’s straightforward, and the flexibility to handle edge cases without rewriting everything.
The success rate for pure no-code automation depends heavily on how well-structured the website you’re automating is. If it’s a modern, API-driven site with predictable patterns, the Copilot does remarkably well. If it’s a legacy site with inconsistent structure or heavy JavaScript rendering, you’ll hit limitations faster.
I tested this on a procurement workflow that involved three websites with different platforms. Two of them, the Copilot generated workflows that ran without modification for months. The third required custom error handling and fallback logic that I had to add manually. The advantage is that adding those custom pieces was straightforward—I didn’t have to rebuild the whole thing, just supplement the generated workflow.
The no-code claim is accurate for the majority of real-world browser automation tasks. Most web interactions follow predictable patterns: navigation, form submission, data extraction, and conditional branching. AI-generated workflows handle these standard patterns robustly.
Where you’ll typically need custom code is when you’re working with legacy systems, heavily obfuscated JavaScript, or workflows that require domain-specific logic. The Copilot might generate 90% of your workflow correctly, and then you add 10% custom logic for the unique parts. This is still substantially faster than hand-coding the entire workflow.