In December 2025, I wrote about a similar experiment in the same project, with a much smaller scope. At that time, my conclusion was that while implementing a simple, small UI component with AI and Figma MCP worked quite well, it was surprising how badly it handled the implementation of a full page. The small UI component generation wasn't perfect either. I could get a ~90% "close enough" output that I could quickly align to the requirements by hand. But when I asked AI to implement a simple login page that contained only already-existing components, even with Figma MCP, the result was disappointing. The layout was far from the design, and it hallucinated elements that weren't in the design at all. No matter how I prompted, it just produced different hallucinations. Which I really don't understand, because Figma MCP provides a structured description of the design. In the end, I spent much more time experimenting with AI than it would have taken to puzzle the components into their places by myself within a few minutes.
My current experience is still not flawless, but I'm amazed by the improvement in this area over the past 3 months. I managed to implement a whole complex page, with existing and new components, that I had estimated at 48 hours, in just 8 hours. Not in one iteration, not 100% AI-generated, not without refactoring and human code reviews, but the velocity is impressive.
After having satisfying experiences with Opus 4.6 UI component implementation, I was eager to retry a full-page AI implementation experiment. When you don't have a strict specification, it's easy to vibe-code a fair-looking result, but it's hard to evaluate how well the output matches the client's needs. That's why I chose a project where we had clear requirements:
These are strict, structural anchors that provide clear and easy verification of the result.
The state of the project when I ran my experiment:
Claude.md that my colleagues had been using for months.Unfortunately, this was a client project we built at Bobcats Coding, so screenshots, product details, and the repository stay private. But I'm going to write about everything else.
The better you specify, the better outcome you can expect. This isn't a new directive; it was true before AI coding as well. But with agentic engineering, specification is the new code.
So I spent ~1 hour specifying the task as my initial prompt. I gave a general context about the page we were building, linked the design of the whole page in Figma and the components one by one. I gave a clear specification for each element of the page: which API endpoint it gets its data from, what it represents, how it should work. I specified all the page actions as well. What should happen when a button is clicked, when a dropdown element is selected, and so on. I also instructed Claude to generate every new UI component in a reusable way within our UI library, test them, and provide Storybook stories and documentation.
I asked AI to create an implementation plan that multiple agents could work on in parallel (because I was curious how this would work). I required a contract-first approach so that the results of the asynchronously working agents could be integrated at the end.
Opus 4.6 worked for 9 minutes to create the plan. It correctly found all the files in the project that it needed to modify and the workspaces and folders where it should create the new files.
It separated the work into 4 agents with clear responsibilities, tasks, and restrictions: