Govern AI agents with Fabric semantic models and Copilot Studio
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Use Power BI semantic models as the governed calculation layer for AI agents, with Fabric data, Copilot Studio reasoning and explicit security boundaries.
TL;DR
Power BI semantic models provide governed calculations, relationships and business rules so Copilot Studio and data agents can reason over financial and operational data without inventing metrics. The proposed architecture has five layers: Data, Logic, Tools, Skills and Governance, with permissions and compliance applied to agents as digital employees.
Original on M365 FM. Read the original · suggested by Mirko Peters
This is our own summary, not a republication or full translation.
Governance takeaway
- Makers: Use approved Power BI semantic models for agent answers so calculations and KPIs remain consistent.
- Admins/CoE: Define agent roles, permissions and security boundaries across the five-layer architecture because agents need governed context.
- Security/Compliance: Apply automated compliance controls and review access to the underlying enterprise data before enabling conversational insights.
- Leadership/Business: Require a single source of truth for financial metrics so personalized AI insights do not create conflicting versions of business performance.
Frequently asked questions
Why do raw language models produce unreliable finance numbers?
Asking an ungrounded AI model to analyze a quarterly P&L statement often results in hallucinated figures and conflicting versions of the truth.
Which five layers make up the governed agent architecture?
Data, Logic, Tools, Skills and Governance. The data layer rests on trusted enterprise data in OneLake and Delta tables.
What does Direct Lake contribute to this design?
Direct Lake lets Power BI query Delta format data stored in OneLake directly. This eliminates data duplication and repeated import refreshes while maintaining high analytical performance.