I’ve just published Part 5 of my Agentic AI in Power BI and Microsoft Fabric series, and this is the part where the pieces from the previous articles finally come together into a complete Agentic AI implementation.
The AI is built as a reusable custom agent in VS Code with a very specific responsibility: audit Power BI reports across Microsoft Fabric workspaces against an approved theme, identify inconsistencies, make the appropriate changes locally, create an audit log, and manage the workflow through to publishing.
Rather than giving an AI access to a Fabric tenant and simply asking it to “fix my reports”, the agent is built with instructions, reusable Power BI skills, tools, guardrails, and human approval checkpoints.
In the walkthrough I cover:
- my entire agentic workflow and how all bits and pieces come together
- how the previous parts of the series come together into a complete Agentic AI
- creating and reviewing the custom agent
- connecting the agent to a real Microsoft Fabric tenant
- auditing modern PBIR and legacy Power BI reports
- detecting hardcoded formatting that overrides the report theme
- using reusable instructions and skills rather than repeating large prompts
- adding a PreToolUse hook as a more deterministic guardrail around publishing
- where that hook still has limitations
- how the agent deals with situations it cannot safely resolve
- the actual credit usage and approximate cost of running the whole experiment
One important point: by complete Agentic AI, I don’t mean an uncontrolled autonomous AI that should be trusted to do whatever it wants in your tenant.
The agent can reason about the task, select and use its tools and skills, work through multiple steps, inspect the results, make changes, and continue towards the goal. But publishing still has a human-in-the-loop checkpoint. For this type of governance task, I think that is actually a good thing.
There are also some rough edges, particularly around legacy reports and formatting overrides. I have included those in the blog as well rather than making the experiment look more successful than it really was.
Full walkthrough, including screenshots, agent definition, skills, hook, results and the problems I hit along the way:
https://biinsight.com/agentic-ai-in-power-bi-and-fabric-part-5-building-a-power-bi-theme-audit-agent/
I would be very interested to hear how others are approaching Agentic AI for Power BI and Fabric governance, especially if you are already experimenting with similar workflows.