Key takeaways
- AI agents have crossed the reliability threshold for real BI work — not just chat, but multi-step actions against your model.
- Five agents worth building: a DAX author, a model auditor, a documentation agent, a report QA agent, and a refresh & anomaly monitor.
- The setup is the same each time: connect an agent to your model via MCP and your PBIP project files, under Git.
- The golden rule: give examples, not just instructions — and keep a human in the loop. You cannot outsource understanding.
AI agents have quietly become reliable enough to do real work — and Power BI is a perfect place to put them. Not chatbots that answer questions, but agents that take multi-step actions against your model: writing measures, auditing structure, documenting, reviewing reports. Here are five AI agents any Power BI team can build, and how they fit together.
A quick note before the list: the teams who get value from agents aren’t the ones with the cleverest prompts. They’re the ones who feed the agent good examples of their own house style and keep a human reviewing the output. Treat every agent below as a fast junior developer — brilliant, tireless, and in need of review.
1. The DAX Author Agent
The workhorse. Connected to your semantic model through a Model Context Protocol (MCP) server, it turns a plain-English spec — the grain, the edge cases, the naming convention, the folder — into a measure that matches how your team already writes DAX. Because the agent reads the existing model, it copies whatever pattern is already there, good or bad, so a clean model matters more than a clever prompt. You review, test against your checklist, and commit.
2. The Model Auditor Agent
Point this one at a model and let it find the problems you stop noticing: measures with no description, unmarked date tables, bidirectional relationships, unused columns, inconsistent naming, calculated columns that should be measures. Using the model’s own metadata (INFO functions and DMVs) it produces a prioritised report — or, against a PBIP project files under source control, proposes the fixes for you to review. It is the difference between “we think the model is fine” and “here are the twelve things to fix.”
3. The Documentation Agent
The job everyone skips. This agent reads the TMDL behind your model and generates a living data dictionary: what each table and measure means, in business language, plus lineage and a glossary. Run it on every release and your model becomes understandable without you in the room — which is exactly what makes the next analyst (and the next AI agent) productive.
4. The Report QA & Design-Review Agent
Design consistency is where most reports quietly fall apart. This agent reviews a report’s definition against your design system — checking theme adherence, colour and formatting consistency, accessibility (contrast, alt text), and flagging visuals that break the house style before a stakeholder ever sees them. It turns “does this look on-brand?” from a subjective argument into a checklist.
5. The Refresh & Anomaly Monitor Agent
The always-on one. It watches dataset refreshes and the data itself: when a refresh fails, it summarises why in plain English; when a KPI moves out of its expected range, it flags the anomaly with a short “so what” and pushes an alert to Teams or email. Less staring at a refresh history, more knowing the moment something needs attention.
How these agents actually get built
The pattern is the same for all five, and it is the toolchain worth learning once:
- MCP — a Model Context Protocol server is the bridge between an agent (like Claude) and a live Power BI or Fabric model, so it can read and act on the real thing.
- PBIP + Git — with your model as TMDL and report as PBIR under source control, every agent change is reviewable and reversible.
- Copilot — Power BI Copilot covers first-draft DAX and narratives inside the report; the agents above handle structured, reviewable work outside it.
- House-style rules — a small set of examples and conventions the agent follows, so its output is consistent across projects.
The one rule that makes or breaks it
You cannot outsource understanding. Agents are astonishing at generating; they are not accountable for whether the answer is right, matches the business grain, or respects row-level security. That judgement stays with you. Give agents your examples, let them do the volume, and keep a human reviewing what ships. That is the whole game — and it is exactly what we teach in our Power BI in the Age of AI course, which walks through MCP, PBIP, VS Code, GitHub and Copilot hands-on.
Inspired by Marina Mogilko’s “5 AI agents that run my content” — we applied the same idea to Power BI.
Frequently Asked Questions
What is an AI agent in Power BI?
An AI agent is a large language model (like Claude or Copilot) connected to your Power BI model or project — often through an MCP server or the PBIP files — that can take multi-step actions such as writing measures, auditing the model or documenting it, with your review.
Do I need to code to build these agents?
Some setup (an MCP server, PBIP and Git) is technical, but directing the agent is largely plain English. The real skill is giving good examples of your house style and verifying the output, not writing the agent from scratch.
Are AI agents safe to run against a production model?
Only with guardrails. Keep a human in the loop, work on PBIP files under source control, and never let an agent publish to production or change security unsupervised.
What is the difference between Copilot and an AI agent?
Copilot works inside Power BI for first-draft DAX and narratives. An AI agent works outside it — against your model or PBIP files via MCP — for structured, reviewable changes like batch measure creation or model audits.
How do we get started with AI agents for Power BI?
Start with PBIP plus Git and an MCP connection, pick one repetitive task such as model auditing, give the agent your house-style examples, and keep every change under review. Our Power BI in the Age of AI course walks through the full setup.
