Power Apps AI agents learn from user fixes in new server feature
Microsoft has launched closed-loop learning in its Power Apps MCP server, an enterprise backend for AI agents. The feature starts with a data entry tool, where user corrections sent through the Agent feed build persistent memory. This memory applies automatically to future runs using genetic-Pareto optimisation, a method that balances multiple goals like accuracy and speed. Tests on invoice datasets showed manual edits falling from 64 percent to 48 percent of fields. The agent's F1 score, a measure of precision and recall, improved by 8.2 points. It is now in public preview but limited to data entry. Customer trials continue, with plans to expand beyond this tool.
Before this, AI agents in Power Apps needed manual tweaks or full retraining after every error, trapping builders in endless fix loops even on simple data tasks like expense logs from SharePoint. Now corrections turn into shared, structured rules that deploy across the organisation without setup, cutting repeat work by over 25 percent in tests. For teams building internal tools, it means data entry apps shift from fragile prototypes to reliable ones that get better in production, without you becoming an AI expert.
Analysis
This preview won't fix your gallery filters or pixel-perfect screens, so skip the agent hype and prove low-code works first. Build a bare-bones canvas expense form that Patches to a SharePoint list end-to-end, then show your boss it runs without AI.
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