Agentic Power BI Development
AI agents can edit Power BI models.
Can you control what they do?
SemanticOps lets agents inspect, change, test, and refactor semantic models through governed, policy-checked, auditable, and reversible workflows.
The shift
From chat assistant to model operator
A chat assistant suggests DAX. An agent writes it, runs it, evaluates the result, and applies a fix — without waiting for you to copy anything.
The gap
Generic MCP exposes tools.
SemanticOps governs their use.
When a tool call can modify production metadata, the question is not just whether the call is possible — it is whether it should be allowed.
01
Can this tool be called?
02
In this environment?
03
By this user?
04
Against this model?
05
With these arguments?
06
After which tests?
07
With what audit trail?
08
With what rollback point?
Risk model
Why agentic workflows create new failure modes
Agents are powerful because they can take action. That is also why they are risky without the right controls in place.
Multi-step chains before human review
An agent can inspect, plan, modify, and test in a single workflow before a human sees any result. Each step compounds the risk.
Tool calls modify real metadata
Unlike a chat suggestion, an agent tool call applies changes directly. There is no diff to review before the edit lands.
Overconfident AI edits
AI may apply a DAX change that compiles but returns wrong results under specific filter contexts — and continue without flagging the issue.
Sensitive data in prompt context
When an agent runs a query to debug a measure, the result set — including customer names, margins, or salaries — may enter the AI context.
Hidden downstream dependencies
An agent renaming a measure does not know which reports, other measures, or roles depend on it unless explicitly told.
No repeatable approval process
Manual approvals do not scale with iterative agentic work. Enterprises need a consistent, enforceable control layer.
Workflow
A governed agentic development workflow
Every operation is checked, masked where needed, tested, logged, and recoverable.
Agent inspects the semantic model
Read-only by default
Agent proposes a refactor or change
SemanticOps runs impact analysis
What depends on this object?
Policy engine checks the proposed action
Allowed, gated, or denied?
Sensitive query results are masked
PII and numeric values hidden before AI context
Agent applies the change
Within the approved scope
Test Runner validates expected behavior
Measures, tables, RLS, OLS
Audit log records tool calls and outcomes
Tamper-evident
Rollback point is available if needed
Snapshot taken before change
Enterprise
Enterprises do not need uncontrolled agents.
They need governed agents.
SemanticOps lockdown modes, policy engine, audit logging, and role-based controls give enterprise teams the evidence and enforcement they need to approve agentic workflows across business-critical semantic models.
Build a governed agentic Power BI workflow.
Give agents the access they need with the controls your team requires. Free to start, no credit card required.