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Microsoft Fabric Functions: How Data Agents Unlock Trusted Self-Service at Scale

By Ralph Jaquez 

About the Series Microsoft Fabric Functions is Interloop’s deep dive into the features that make Fabric not just powerful, but practical. We’re cutting through the noise to show how these tools drive real business results. Whether you're leading strategy or scaling systems, this series is built to help you turn complexity into clarity – and manual tasks into momentum. 



Not Just a Translator — A Game Changer for Self-Service 


There’s been a lot of buzz lately around Fabric Data Agents — and for good reason. If you’ve read the Microsoft docs, you already know the core pitch: ask questions in plain English, get answers from your data without writing SQL, DAX, or KQL. 


But the real value of a Data Agent isn’t just in translating language. It’s in how it changes the way people access and trust data — especially for teams that have long been stuck behind bottlenecks. 


Why You’d Actually Use One 


Let’s walk through a (fictional but familiar) example: PalmettoEV, a growing electric scooter and small EV rental company operating in cities across the U.S. and Europe. 


PalmettoEV’s data lives in multiple systems: 


  • App usage in Firebase 

  • Kiosk activity in SQL Server 

  • Loyalty data in MySQL 

  • Vehicle telemetry in BigQuery 


All of it has been harmonized in Microsoft Fabric so the team can analyze trips, vehicles, customers, and payments in one place. 


Before Data Agents, a simple question like “How many unique riders did we have in Barcelona last month?” meant: 


  • Submitting a request to the data team 

  • Waiting for someone to write and validate a query 

  • Going back and forth when definitions didn’t match 


With a properly configured Fabric Data Agent, that same question can be asked in natural language — and answered instantly. 


Why? Because the Data Agent already knows: 


  • Which tables to query 

  • How the business defines a “rider” 

  • What counts as “Barcelona” 

  • Who is allowed to access what 


Where It Shines for Teams Like PalmettoEV 


Think of the Data Agent as a domain-specific concierge for your data. 

It allows users to ask targeted, business-relevant questions like: 


  • “Which scooters had repeated downtime last week in Madrid?” 

  • “List riders who booked more than three trips in Paris this month.” 

  • “Show daily revenue in Austin for the last 30 days.” 


Each of these works — and delivers trusted answers — because the agent is built on a clean, business-aligned model. 


The Real Magic: Your Data Model 


Here’s the hidden truth behind every successful Data Agent: the magic isn’t in the AI — it’s in the model. 


For PalmettoEV, that meant doing the foundational work: 


  • Harmonizing vehicle IDs across systems to track downtime 

  • Defining a “trip” in one consistent, business-accepted way 

  • Linking customers, trips, and payments with clean, trusted relationships 


When the underlying model is strong, the agent becomes a reliable tool — not a novelty. Everyone gets the same answer to “What’s our average trip duration per city?” — and it’s the right one. 


When the model is weak or inconsistent? You get vague results, mismatched metrics, and frustration. That’s when trust erodes, and teams revert back to manual queries (or give up altogether). 


Why This Matters for Mid-Market Leaders 


Fabric Data Agents make it possible to scale trusted, self-service access to data — without overburdening your data team or compromising on accuracy. 


But here’s the catch: self-service only works when the answers are right. 


That’s why the real win isn’t just spinning up a Data Agent — it’s investing in the modeling work that makes it useful: 


  • Aligning terms across teams 

  • Cleaning up relationships 

  • Standardizing definitions 

  • Harmonizing across platforms 


Done right, you get a tool that meets users where they are — and gives them answers they can act on. 


This Is Where We Come In 


At Interloop, we specialize in building harmonized, business-friendly models that make tools like Fabric Data Agents shine — whether your data lives across ERPs, CRMs, telemetry feeds, or a mix of cloud and on-prem systems. 


Our goal is simple: make sure when your users ask a question, they get an answer they can trust. 



Final Word 


Fabric Data Agents aren’t a shortcut around modeling — they’re the best reason to double down on it. 


If you want analytics your team can actually use (and trust), this is a powerful tool worth exploring. But like most things in data, it works best when the foundation is solid. 


Let’s make sure it is. 


Curious whether your data model is ready for a Fabric Data Agent? Let’s get you there. Get looped in today. 

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