I’m not talking about a simple single-action test here. I mean a real, multi-step end-to-end workflow. Login, navigate to a page, fill out a form with multiple fields, handle dynamic dropdowns, submit, and validate the success state.
I’ve tried this a few times with mixed results. Simple workflows? Yeah, those come out pretty clean. But when I tried describing a complex checkout flow with error handling and retries, the generated code was incomplete. It missed entire sections of logic and didn’t quite understand the flow dependencies.
Maybe I’m not describing it clearly enough in plain English. Or maybe there’s a sweet spot where full end-to-end generation actually works well. I’m genuinely curious if anyone has pushed this to real complexity levels and gotten workable output, or if the Copilot works best for generating smaller, reusable components that you then stitch together manually.
What’s been your actual experience with this? Did the multi-step workflows it generated actually run without heavy tweaking?
I’ve built complete checkout flows, multi-page authentication chains, the whole thing. The secret isn’t treating it like you’re talking to a human. You need to think in components.
Instead of one massive description, I break it down. “Generate the login workflow,” then “generate the form validation,” then “generate the success check.” Each one comes out clean and focused. Then I wire them together in the canvas.
The AI Copilot handles each piece way better than trying to describe everything at once. I’ve noticed that when you give it one clear objective per description, the generated code is production ready. When you dump everything into one paragraph, it gets lost.
For error handling specifically, I describe the happy path first, then ask the Copilot to add retry logic. Two separate generations. Works way better than trying to describe both simultaneously.
Full complexity flows do work, but you need to respect how the AI thinks about workflows. Break them into logical units, describe each unit clearly, then compose them. That’s when you get reliable, reusable code.
I actually tried brute forcing a full checkout flow description all at once. Took me three different attempts and manual code editing to get something that actually worked. Frustrated doesn’t even cover it.
Then I realized something. The Copilot was generating each piece correctly, but when I crammed everything into one description, the AI lost track of the dependencies between steps. The login would generate fine. The form filling would generate fine in isolation. But together? It tried to fill the form before waiting for the login redirect to complete.
Once I stopped thinking of my workflow as one monolithic thing and started breaking it into named sub-workflows, everything changed. GeneratedLogin, GeneratedFormFill, GeneratedValidation. Each one confirmed working independently. Then glue them together in the main workflow.
The full complexity absolutely works, but only if you respect the boundaries. Treat each logical section as its own mini-problem, not as one giant problem.
Complex workflows coming from the Copilot tend to miss error handling details and edge cases unless you specifically mention them. I’ve generated full checkout processes, but I always end up adding manual exception handling and retry logic afterward. The AI does great with the happy path but struggles with contingency planning. What I do now is generate the main flow first, validate it works, then ask the Copilot specifically to add error handling on top of the generated code. Separate prompt, separate generation. That two-phase approach catches most of what a single monolithic prompt would miss.
Multi-step workflow generation succeeds when you provide sufficient context about each step’s preconditions and exit conditions. Without that detail, the AI fills gaps with assumptions that don’t match your actual site behavior. I’ve successfully generated complex flows by describing not just the actions but the expected states between actions. After login, the dashboard appears, confirmed by URL change plus element visibility. After form submission, the API responds with success. These state descriptions force the AI to generate stronger dependencies. The generated code becomes more robust because it encodes actual verification points, not just action sequences.
break complex flows into smaller parts. generate each part separately, then wire them 2gether. way better results than dumping everything in 1 description.