A practical guide for Power BI analysts and business decision makers
Summary
Fabric Data Agents let you ask questions in natural language and get answers from your organisational data. They show you the underlying query (in SQL, DAX, or KQL) used so you can see how the answer was produced, and they return the results in a clear table so you can easily validate them. With light tuning, such as adding instructions, synonyms, and example queries, agents become fast, helpful copilots for everyday data analysis, while giving you the transparency to verify results.
What is a Fabric Data Agent?
Fabric Data Agents let users ask natural‑language questions and get answers directly from organisational data, along with the steps the agent used to produce them.
They act as a conversational layer on top of your existing data, helping teams move faster while still operating within Microsoft Fabric’s security and governance model.
Behind the scenes, a Data Agent interprets the question into the relevant query (SQL, DAX, or KQL) and runs it against the data sources you attach. The agent returns both the answer and the exact query it used, giving analysts full transparency.
Unlike Copilot in Power BI, which operates within a single Power BI report, Data Agents can sit across multiple Fabric items, making them ideal when insights span more than one data model.
How do Fabric Data Agents work?
To get the best results, your data model needs some preparation—clear naming, consistent logic, and a few example queries. This helps the agent interpret your data correctly and improves the quality of responses.
1. Choose the data sources
Specify the data sources that you want the agent to use and select the relevant tables. E.g. For an agent that will answer questions related to company revenue, you want to include Customer data, Sales transaction data, and Inventory or Services sold data, but you might not need to include payroll or banking data.
You can include multiple sources, but the more you include, the more important tuning becomes.
2. Tune the agent (this is your “secret sauce”)
You can provide instructions that help the agent understand your data and produce better results, e.g.:
- Agent instructions: Define key business logic (e.g., “COGS = SalesAmount – LineProfit”, “Sales = SUM(SalesAmount)”).
- Synonyms & friendly names: Align technical column names with business language.
- Example queries: Show how tables relate so the agent learns the right join patterns (e.g., between Sales and Inventory).
3. Test the agent
Ask questions and review the answers. The agent returns both the result and the exact SQL/DAX/KQL query it executed so analysts can validate the logic.
4. Publish and reuse
Once you're happy with it, publish the agent so your team can use it within Fabric. Agents can also be extended into Teams via Copilot Studio or integrated into Azure AI Foundry for more advanced scenarios.
How do Fabric Data Agents help your organisation?
Most organisations face the same challenge:
- You have lots of valuable data.
- It’s still hard to get clear answers quickly.
- And teams often rely on technical expertise or manual work to interpret it.
Fabric Data Agents remove that barrier by making your data far easier for everyone to work with:
- Analysts can ask questions in plain English, and the agent generates the logic behind the scenes, along with a clear answer they can check. It also provides a useful way to learn by showing the exact logic it used.
- Leaders get fast, trustworthy insights drawn from governed organisational data, not spreadsheets or numbers copied into emails. They can ask questions like:
- “Which region performed best last quarter?”
- “What products are impacting margin?”
- “How do sales compare year-over-year?”
- Teams stay secure and compliant, because Data Agents respect existing permissions, ensuring people only see the data they’re meant to. When agents are published to other workspaces, e.g., Microsoft Teams, users can access data where they do their work, saving time and reducing the friction of switching between apps.
Tuning tips that help make your agent smarter
- Start with a “golden” dataset which is the most trusted and verified organisational data. Keep the source clean and well‑named.
- Write clear instructions. Define key KPIs and calculations.
- Clarify ambiguous labels, e.g., “Branch = ResellerName.”
- Add example queries for tricky joins.
- Add business‑friendly synonyms.
- Iterate, test, and publish. Validate answers against known reports.
Governance, performance, costs and limitations
- Security & privacy: Users only see what they’re permitted to see; Row Level Security (RLS) and workspace access permissions apply, which is critical for enterprise rollouts.
- Capacity usage: Expect noticeable Fabric capacity consumption during experimentation; consider isolating capacity for Copilot/agent pilots.
- Change management: Train teams on prompt patterns, validation discipline, and responsible use.
When using AI for data analytics, the quality of the underlying data and the data model are critical. Fabric Data Agents can still make mistakes, so you should always validate answers.
Final thoughts
Fabric Data Agents represent an exciting step toward making organisational data easier, faster, and safer for everyone to use. They give analysts a powerful new assistant and offer leaders a way to explore trusted insights without relying on shadow spreadsheets or long report-writing queues.
Like all AI systems, Data Agents can occasionally return incomplete or incorrect answers. The best results come from well-prepared data models, thoughtful tuning, and a culture where validation remains part of the workflow.
Even with these considerations, the potential is clear: Fabric Data Agents can significantly reduce friction in how organisations access insight. As Microsoft continues to refine and expand this feature, it’s a promising addition to the toolkit of any business aiming to make data-driven decisions more quickly and confidently.
Watch our recent webinar: Standalone Copilot in Power BI and Fabric Data Agents. Learn more about Fabric Data Agents and see ‘live’ demos.
