June 2026 updates add closed-loop learning to Power Apps agents
Reviewed by Helen Jones
A five-minute community video released 21 June summarises the monthly release. It highlights a new MCP server capability that lets agents learn from their own past runs and Power CAT sample apps that demonstrate governance patterns. The coverage stays at overview level. No step-by-step implementation guidance or performance numbers are provided. The video positions these changes as incremental improvements to agent reliability and oversight.
Before these updates, agents in Power Apps produced one-shot suggestions that makers had to verify manually each time. The risk was that a fragile formula or poorly delegated gallery would be copied into production without anyone noticing until the list grew. Closed-loop learning changes the dynamic because the agent can now reinforce whatever pattern it first generated. If the initial SharePoint connection already hits delegation limits or uses non-performant lookups, the agent will treat those patterns as successful and repeat them at scale.
Analysis
Run your largest gallery against the full expected dataset size right now and fix every delegation warning before you let any agent touch the app. One brittle pattern locked in by closed-loop learning will cost more time than the samples will ever save.
Pulse published by Collab365 Spaces, reviewed by Helen Jones on . Cite as "June 2026 updates add closed-loop learning to Power Apps agents", Collab365 Spaces. 1 source referenced.