From Figma to a full Azure DevOps backlog - with a human approving every page
How we used an AI agent connected to Figma and Azure DevOps to turn a design into a complete, reviewed backlog - and then into working pages.
For our new company website, an AI agent read the Figma design and turned it into a full Azure DevOps backlog - epics, user stories and tasks - and then built the public pages from that backlog. The agent did the decomposition; a human approved every page before anything reached the backlog.
Turning a design into a backlog is slow, careful work. Someone has to go through every page, every block and every state, and write tasks a developer can pick up without asking questions. It is necessary work, and it is exactly the kind of structured, repetitive work an AI agent does well - as long as someone who knows the product checks it.
We connected two systems to the coding agent (Claude Code) through MCP - the Model Context Protocol, a standard way to give an AI agent access to external tools:
Because a backlog is much easier to fix before it exists. The agent wrote to local files first, so a misread design cost me a comment, not a cleanup of dozens of work items. Approving one page at a time also kept each review small enough to do properly. Reviewing an entire generated backlog in one sitting is how you end up approving things you did not really read.
It also meant the backlog was one I could stand behind. Every item in Azure DevOps had been seen and accepted by a person who knew what the page was supposed to do.
The agent did the decomposition. The decisions stayed with people.
A complete, structured backlog for the website - epics, user stories and tasks with descriptions and links to the design - and then the website's public pages, built from that backlog. Because the tasks pointed back to Figma, anyone picking one up could see the exact design it came from.
This works best when the design is reasonably final and the team already uses Azure DevOps or a similar tracker. It does not remove the project manager - it changes their job from typing tasks to judging them. That is the pattern behind how RemSoft.Dev delivers with AI: agents do the volume, and a human approves every step that matters.
Our engineers can help map this to your stack.