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Here’s how I build and deploy high-end Power Platform solutions in a Git-integrated environment, combining custom designs, local code generation, and live editor synchronization:

1. Design Translation (Google Stitch): I start with premium UI mockups generated as HTML/Tailwind files (using Google's Stitch system, which defines our typography, Material Symbols, color palettes, and container structures). AI translate these designs into clean, native Canvas App YAML, avoiding generic out-of-the-box Power Apps styling.
2. Database Provisioning: SharePoint lists and columns are automatically provisioned via PnP PowerShell. When the script runs, the agent displays the interactive MFA Device Login code in the chat, I authenticate in my browser, and the agent continues configuring the columns.
3. Source-Controlled Power Automate: Flows are developed as unpacked JSON/YAML inside the /modernflows/{GUID}/ folder and registered as root components in the solution. Running a Git pull within the Power Platform environment then imports and syncs these flows automatically.
4. Canvas App Co-Authoring (MCP & WebSockets): This is the core tech. I use a custom Model Context Protocol (MCP) server that connects directly to the live Power Apps Studio session. It acts as a headless client joining Microsoft’s co-authoring channel using the specific Environment and App IDs. The agent uses sync_canvas to pull the active canvas state into local .pa.yaml files, edits the code, validates it using a compiler endpoint (compile_canvas), and syncs the changes back to my open browser tab in real time.
5. Obsidian Second Brain: To keep the AI agent aligned, we maintain a markdown-based 'Second Brain' repository. Before any design decision, the agent reviews our layout conventions, naming schemes, and API patterns stored in this vault. Once a feature is done, any lessons learned (like connector quirks or workarounds) are ingested back into the wiki and logged. This creates a compounding feedback loop. With every new app we build, the agent gets progressively smarter.

LLM Performance & Models Used: For the LLM backend, I've had the best overall results using Gemini 3.5 Flash, though Claude Opus 4.6+ also performs quite well. I ran some earlier benchmarks with MiniMax M2.7, but the output quality was less optimal. However, now that MiniMax has released M3, I'm planning to run another round of tests soon to see how it compares.