Business intelligence has always had a translation problem. Data teams speak SQL. Marketing speaks campaigns. Finance speaks revenue. And somewhere between those languages, decisions get delayed.
Microsoft Fabric data agents solve this by removing the technical barrier entirely. Released in preview throughout 2025, these AI-powered assistants transform how organizations access enterprise data. Users ask questions in natural language. The agent translates that into SQL, DAX, or KQL depending on the data source. Then it executes the query and returns structured answers.
The shift matters because most companies already have the data infrastructure. What they lack is accessible insight. Employees who could make faster decisions if they could just query the warehouse themselves. Teams who understand their business metrics but not database schemas.
Microsoft built these agents on Azure OpenAI Assistant APIs, integrating them directly into the Fabric platform. They work across lakehouses, warehouses, Power BI semantic models, and KQL databases. All while maintaining the same security controls and user permissions your organization already has in place.
The result is conversational analytics without compromising governance. Self-service intelligence without the usual chaos.
TL;DR
Microsoft Fabric data agents are AI-powered assistants that let users query enterprise data using natural language instead of SQL, DAX, or KQL. Built on Azure OpenAI APIs, they translate plain English questions into accurate queries across lakehouses, warehouses, Power BI models, and KQL databases. These agents maintain enterprise security, enforce existing permissions, and provide instant insights without compromising governance. Organizations gain self-service analytics while technical teams focus on strategic work.
What Are Microsoft Fabric Data Agents? Understanding the Foundation
Microsoft Fabric data agents function as intelligent intermediaries between business users and enterprise data. They’re built directly into the Fabric ecosystem, which means they already have access to your organization’s data infrastructure. When someone asks a question, the agent processes it through natural language understanding and determines which data source holds the answer.
The assistant operates within your existing security framework. It doesn’t bypass permissions or create new access points. Instead, it uses the same credentials and authorization rules already in place. What changes is how people interact with that data. Typing a question replaces writing queries.
Built on Azure OpenAI Assistant APIs for Natural Language Processing
The underlying technology relies on Azure OpenAI Assistant APIs, specifically designed for agentic behavior. These APIs handle the complex work of understanding context, maintaining conversation flow, and generating accurate responses. Microsoft manages the OpenAI integration behind the scenes, so organizations don’t need to provision their own AI resources or manage API keys.
What makes this architecture effective is the combination of large language models with Microsoft’s enterprise data tools. The LLM understands the question. The Fabric platform knows where the data lives and how it’s structured. Together, they bridge the gap between casual business questions and technical database queries. The agent essentially becomes a translator that speaks both languages fluently.
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Democratizing Data Access Across Organizations
Data democratization sounds like corporate jargon, but the concept is straightforward. Right now, insights are bottlenecked. Business users depend on data teams to run queries, build reports, and generate analyses. That dependency creates delays. It also limits the questions people ask because they don’t want to burden already overwhelmed technical teams.
Fabric data agents remove that friction. Marketing can check campaign performance without waiting for analytics. Operations can monitor KPIs without filing tickets. Finance can explore variance trends without learning SQL. The goal is making data accessible to everyone who needs it, when they need it, without sacrificing governance or security. Organizations keep their controls intact while expanding who can actually use the insights they’ve invested in building.
Why Microsoft Fabric Data Agents Matter: Key Benefits & Business Impact
1. Breaking Down Technical Barriers
Here’s the thing: most of your valuable data is locked behind a technical wall. Business users know exactly what they need to find out, but they can’t get to it themselves. Why? Because they don’t speak SQL, DAX, or KQL.
This creates a frustrating dependency on specialized teams. These teams become bottlenecks—not because they’re slow or unresponsive, but simply because they’re the only ones who can translate business questions into database queries.
No more query language expertise required. Users just type questions in plain English like “What were our top selling products last quarter in the Northeast region?” They get direct answers. No code needed.
Your IT and BI teams get their time back. Think about it: technical teams can finally spend less time running routine queries and more time on strategic projects. Meanwhile, business users get immediate answers to their ad hoc questions without waiting in line.
Instant exploratory analysis becomes possible. Anyone can test hypotheses, drill into trends, or validate assumptions on the spot. No more waiting for report development cycles or formal analytics requests to work their way through the queue.
Traditional business intelligence operates on request cycles. And those cycles are slow.
Someone needs information. They submit a request, wait for development, and review the output. Often, they go back for refinements. That whole process? It can take days or weeks.
Meanwhile, market conditions change. Opportunities pass. Decisions get made with incomplete information.
Real-time query responses replace those painful delays. Questions that previously required days of BI team effort now get answered in seconds. This compresses decision-making timelines significantly—and that matters when timing is everything.
Self-service analytics without compromising governance. Business users gain independence to explore data freely. But here’s what’s crucial: all existing security policies, row-level restrictions, and compliance controls remain enforced automatically. You don’t sacrifice control for speed.
Business continuity when resources are tight. Organizations can maintain their analytical capabilities even when technical staff are unavailable, traveling, or focused on other priorities. The work doesn’t stop when key people aren’t available.
3. Enterprise-Grade Security & Governance
Let’s address the elephant in the room: security.
The biggest concern with self-service analytics is usually “Will users see data they shouldn’t?” Or “Can they accidentally modify something?” Or “Will sensitive information leak?”
Fabric data agents address these concerns head-on. They operate within your existing security frameworks rather than creating new access paths. That’s a critical distinction.
Automatic enforcement of Row-Level Security (RLS) and Column-Level Security (CLS). Users only see data they’re authorized to access—period. This is based on their Microsoft Entra ID credentials and existing workspace permissions. No manual security configuration needed on your part.
Read-only access prevents data modification. Agents can only query and retrieve information. They can never update, delete, or modify your underlying data sources. This eliminates the risks that come with write access.
Full integration with Microsoft Purview compliance. All data access follows your Information Protection policies. It maintains audit trails for compliance reporting. And it connects with Insider Risk Management for suspicious activity detection. Your governance framework stays intact.
How Do Microsoft Fabric Data Agents Actually Work?
1. Question Parsing and Validation (Security, RAI Policies)
When you type a question, the agent doesn’t immediately start querying your data. That would be reckless.
First, it analyzes what you’re asking to make sure the request is legitimate and safe. This step uses Azure OpenAI’s responsible AI framework to screen for potential security issues, policy violations, or attempts to access restricted information.
Natural language interpretation identifies what you actually want. The agent breaks down your question to understand what you’re really asking. Is this a request for sales data or customer lists? Are you filtering for specific time periods or looking at overall trends? The agent figures this out.
Responsible AI screening blocks inappropriate requests. Questions that attempt to bypass security, extract sensitive information inappropriately, or violate usage policies get flagged and rejected. This happens before any data access occurs.
Initial permission checks verify your authorization. The system confirms you have basic access rights to the workspace and data sources before proceeding. Unauthorized query attempts don’t even reach the database layer.
2. Data Source Identification (Using Your Credentials)
Once your question passes validation, the agent needs to figure out where the answer lives.
Your organization likely has data spread across multiple sources—lakehouses, warehouses, Power BI models, real-time databases. The agent evaluates all available options using your specific credentials. This determines which sources you can access and which ones contain relevant information.
Schema access follows your permission levels. The agent only sees table structures and metadata from data sources where you already have read permissions. It can’t even discover data you’re not authorized to view. This is fundamental to maintaining security.
Multi-source evaluation finds the best match. The system compares your question against schemas from lakehouses, warehouses, Power BI semantic models, KQL databases, and ontologies. Then it identifies which source contains the most relevant data for your question.
Context from agent instructions guides selection. Custom instructions you’ve provided about business terminology, preferred data sources, or specific use cases help the agent choose the right location when multiple options exist. It learns from your preferences.
3. Query Generation (NL2SQL, NL2DAX, NL2KQL Translation)
After identifying the right data source, the agent translates your plain English question into the appropriate query language.
This translation depends entirely on what type of data source was selected. Relational databases need SQL. Power BI semantic models require DAX. Real-time databases use KQL.
Natural language to SQL for lakehouses and warehouses. Questions about raw transactional data or structured tables get converted into SQL queries. These can retrieve records, calculate aggregations, or join multiple tables based on what you asked.
Natural language to DAX for Power BI semantic models. When you’re querying curated business metrics or pre-built data models, the agent generates DAX expressions. These leverage existing measures, hierarchies, and relationships already defined in your semantic layer.
Natural language to KQL for real-time databases. Questions about logs, telemetry, event streams, or time-series data get translated into Kusto Query Language statements. These are optimized for analyzing large volumes of streaming information.
4. Query Validation (Syntax and Security Checks)
The generated query doesn’t execute immediately. There’s another checkpoint.
Before running anything against your actual data, the agent validates that the query is properly formed and complies with all security protocols. This prevents malformed queries from causing errors. It also ensures no security rules get bypassed during execution.
Syntax verification ensures the query is correct. The system checks that the generated SQL, DAX, or KQL statement follows proper grammar rules. It won’t cause execution errors or return unexpected results due to logical mistakes.
Security protocol enforcement at the query level. Additional security checks confirm the query doesn’t attempt to access restricted columns, bypass row-level filters, or circumvent any data protection policies configured at the source. Multiple layers of protection.
Responsible AI policy application prevents harmful queries. Final screening ensures the query doesn’t extract sensitive information inappropriately or violate organizational data usage policies—even if the initial question passed validation. Defense in depth.
5. Query Execution and Response Delivery
With validation complete, the agent executes the query against your chosen data source and processes the results.
But it doesn’t just dump raw query output back to you. Nobody wants that.
The agent formats the response into human-readable language. Often, this includes visualizations, summaries, or explanations that make the data easier to understand and act on.
Secure execution using your identity. The query runs with your credentials and permissions. This means you only get back data you’re authorized to see. All existing security boundaries remain intact throughout the entire process.
Structured response formatting improves readability. Raw database results get transformed into natural language answers, tables, or charts that match how you asked the question. The insights become immediately actionable without requiring interpretation.
Query transparency enables validation and learning. The agent shows you the actual SQL, DAX, or KQL it generated. This allows technical users to verify accuracy, learn query patterns, or refine their questions for better results next time. It’s transparent, not a black box.
How to Create Your First Microsoft Fabric Data Agent: Step-by-Step Guide
Step 1: Verify Your Prerequisites
Before you start, confirm your environment meets the basic requirements. You need either a Fabric capacity of F2 or higher, or a Power BI Premium capacity (P1 or above) with Microsoft Fabric enabled. Your admin must enable three tenant settings: Fabric data agent feature, cross-geo processing for AI, and cross-geo storing for AI. If you plan to use Power BI semantic models, make sure the XMLA endpoints setting is turned on.
Step 2: Navigate to Your Workspace
Open your Fabric workspace where you want to create the agent. Click the “+ New Item” button at the top. In the “All items” tab, search for “Fabric data agent” and select it. You’ll be prompted to provide a name. Choose something descriptive that reflects the agent’s purpose, like “Sales Analytics Agent” or “Operations Dashboard Assistant.”
Step 3: Add Your Data Sources
Once created, the OneLake catalog appears automatically. Select up to five data sources in any combination: lakehouses, warehouses, Power BI semantic models, KQL databases, or ontologies. Add each source individually by selecting it from the catalog and clicking “Add.” You can filter by data source type to find what you need faster.
Step 4: Select Relevant Tables
After adding sources, the Explorer pane on the left displays available tables. Use checkboxes to make specific tables available or unavailable to the AI. Only include tables relevant to the questions users will ask. This improves response accuracy and reduces processing time.
Add business context through agent instructions. Define key terminology, explain how certain datasets should be used, and provide example queries. These instructions help the agent understand your organization’s specific language and priorities. The more context you provide, the better the responses.
Step 6: Test Your Agent
Start asking questions to validate performance. Try different phrasing for the same question. Check if responses match expected results. Review the generated queries to ensure they’re pulling from the right tables. Gather feedback from colleagues who will actually use the agent.
Step 7: Publish and Share
When testing confirms accuracy, click “Publish” to make the agent available. Set appropriate workspace permissions so authorized users can access it. Generate the REST endpoint URL if you plan to integrate the agent with other applications like Copilot Studio or Azure AI Foundry.
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Microsoft Fabric Data Agents: 7 Key Industry-Specific Use Cases
1. Banking: Fraud Detection and Risk Management
Fraud analysts need immediate access to transaction patterns without filing IT tickets. Risk officers must evaluate portfolio exposure across multiple accounts and products quickly. Fabric data agents let banking teams query customer behavior, transaction anomalies, and compliance metrics in real time while maintaining strict financial data governance.
- Suspicious activity investigation – Analysts ask “Show me accounts with transactions over $50,000 to foreign entities in the last 72 hours” and get instant results to flag potential money laundering or fraud patterns
- Credit risk assessment queries – Loan officers check “What’s the average debt-to-income ratio for approved mortgages in high-risk zip codes?” to make informed lending decisions without waiting for risk committee reports
- Regulatory compliance reporting – Compliance teams pull specific transaction data for audit requests like “All wire transfers above reporting thresholds in Q4” with complete audit trails automatically documented
2. Healthcare: Patient Care Optimization
Hospital administrators balance patient flow, resource allocation, and quality metrics daily. Clinical teams need insights into treatment outcomes and readmission patterns. Data agents provide healthcare professionals with instant access to operational and clinical data while enforcing HIPAA compliance and patient privacy controls automatically.
- Bed availability and patient flow management – Operations staff query “How many post-surgical beds are available across all campuses right now?” to coordinate patient transfers and emergency department admissions efficiently
- Readmission rate analysis – Quality improvement teams ask “What’s our 30-day readmission rate for heart failure patients by physician?” to identify intervention opportunities and improve care protocols
- Staff utilization optimization – Scheduling managers check “Which shifts had nurse-to-patient ratios below standard last week?” to address staffing gaps before they impact care quality or employee burnout
Store managers need visibility into what’s selling, what’s sitting on shelves, and how their location compares to others. Merchandising teams track trends across regions to adjust buying strategies. Data agents eliminate the lag between questions and answers that often causes missed opportunities in fast-moving retail environments.
- Real-time inventory checks – Store managers ask “Which products are below reorder threshold at my location?” to prevent stockouts of popular items without manually checking inventory management systems
- Cross-location sales comparisons – Regional directors query “How do sales of winter apparel compare between Chicago and Denver stores this month?” to identify regional preferences and adjust distribution accordingly
- Promotional effectiveness analysis – Marketing teams check “What was the conversion rate for our email campaign versus in-store promotions last week?” to allocate promotional budgets toward channels that actually drive revenue
4. Manufacturing: Production Efficiency and Supply Chain
Plant managers track production metrics, equipment performance, and supply chain disruptions across multiple facilities. Quality control teams need quick access to defect rates and root cause data. Fabric data agents connect disparate manufacturing systems to provide unified visibility into operations without complex integration projects.
- Equipment downtime analysis – Maintenance supervisors ask “Which machines had unplanned downtime over 4 hours this month?” to prioritize preventive maintenance investments and reduce production losses
- Supplier performance tracking – Procurement teams query “What percentage of parts from Supplier A arrived late in Q1?” to renegotiate contracts or identify alternate vendors before delays impact production schedules
- Quality defect pattern identification – Quality managers check “Show me defect rates by production shift and product line” to identify whether issues stem from specific teams, materials, or processes needing attention
Network operations teams monitor service quality, outage patterns, and capacity issues across vast infrastructure. Customer service representatives need instant access to account history and service problems. Data agents help telecom companies respond faster to both technical issues and customer inquiries.
- Network congestion identification – Engineers ask “Which cell towers experienced capacity over 85% during peak hours yesterday?” to prioritize infrastructure upgrades in areas with growing demand
- Customer churn risk analysis – Retention teams query “How many customers contacted support more than three times about service issues this month?” to proactively address problems before subscribers cancel
- Service outage impact assessment – Operations managers check “How many business customers were affected by last night’s fiber cut?” to prioritize restoration efforts and communicate accurately with affected accounts
Academic administrators track enrollment trends, student success rates, and resource utilization across departments. Advisors need quick access to student progress data to intervene early when learners struggle. Fabric data agents help educational institutions make data-driven decisions about programs, staffing, and student support.
- At-risk student identification – Academic advisors ask “Which first-year students have GPAs below 2.5 and missed more than 20% of classes?” to provide targeted support before students drop out
- Course capacity planning – Registrars query “What’s the enrollment rate for spring courses compared to last year?” to add sections for high-demand classes or consolidate underenrolled ones
- Program effectiveness evaluation – Deans check “What’s the four-year graduation rate by major and student demographic?” to identify programs needing curriculum improvements or additional student resources
Utility operators monitor power generation, distribution efficiency, and equipment health across extensive infrastructure networks. Field service teams need asset maintenance history before dispatching technicians. Data agents provide energy companies with instant operational intelligence while managing complex IoT and SCADA data streams.
- Predictive maintenance for critical assets – Asset managers ask “Which transformers are showing voltage fluctuations above normal and are over 15 years old?” to prevent failures before they cause outages
- Energy demand forecasting support – Grid operators query “What was peak demand by region during similar weather patterns last year?” to prepare for upcoming demand spikes and optimize generation scheduling
- Outage response coordination – Emergency response teams check “How many customers are currently without power and what’s the estimated restoration time?” to prioritize repair crews and communicate accurately with affected communities
Why Kanerika is Your Trusted Partner for Microsoft Fabric Implementation
Implementing Microsoft Fabric data agents requires more than just technical knowledge. You need a partner who understands both the platform’s capabilities and your specific business context. Kanerika brings that combination as a certified Microsoft Solutions Partner for Data and AI, along with Databricks partnership credentials.
We specialize in deploying Microsoft’s analytics ecosystem including Fabric, Copilot, Power BI, and Purview. Our team holds advanced Microsoft specializations in Analytics on Microsoft Azure and Data Warehouse Migration to Microsoft Azure. This isn’t theoretical expertise. We’ve built production solutions that handle real enterprise data challenges across industries.
Security and quality standards matter when you’re working with business-critical data. Kanerika maintains CMMI Level 3, ISO 27001, ISO 27701, and SOC 2 Type 2 certifications. These aren’t just compliance checkboxes. They represent audited processes that protect your data throughout implementation and beyond.
Our approach combines Microsoft Fabric’s native capabilities with Databricks’ data intelligence platform when your use case demands it. We don’t push specific tools. We assess what you actually need, design solutions that address those requirements, and deploy them with minimal disruption to ongoing operations.
Whether you’re starting fresh with Fabric data agents or integrating them into existing analytics infrastructure, we handle the technical complexity while you focus on the business outcomes. That’s the partnership model that works.
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FAQs
1. What are Microsoft Fabric data agents?
Microsoft Fabric data agents are AI-powered conversational assistants that enable users to query enterprise data using natural language. Built on Azure OpenAI Assistant APIs, they translate plain English questions into SQL, DAX, or KQL queries depending on your data source. Users get instant answers from lakehouses, warehouses, Power BI semantic models, and KQL databases without needing technical expertise.
Do I need to know SQL to use Microsoft Fabric data agents?
No, you don’t need SQL, DAX, or KQL knowledge. Fabric data agents handle all query translation automatically. You simply type questions in plain English like “What were top products by revenue last quarter?” and the agent generates the appropriate database query, executes it, and returns results in human-readable format with explanations.
What data sources can Microsoft Fabric data agents connect to?
Fabric data agents connect to five types of data sources: lakehouses for raw transactional data, warehouses for structured enterprise data, Power BI semantic models for business metrics, KQL databases for real-time event streams, and ontologies for business context. You can add up to five data sources in any combination per agent.
How much do Microsoft Fabric data agents cost?
Microsoft Fabric data agents require either Fabric capacity F2 or higher, or Power BI Premium capacity P1 or above with Fabric enabled. There’s no separate licensing fee for the data agent feature itself. Pricing depends on your existing Fabric or Power BI Premium capacity subscription. The feature is currently in public preview.
Are Microsoft Fabric data agents secure?
Yes, Fabric data agents enforce enterprise security automatically. They use your Microsoft Entra ID credentials, respect existing Row-Level Security and Column-Level Security policies, and maintain read-only access to prevent data modification. All queries follow your organization’s Microsoft Purview governance policies and generate complete audit trails for compliance reporting.
How do I create a Microsoft Fabric data agent?
Navigate to your Fabric workspace, click “+ New Item,” and search for “Fabric data agent.” Name your agent, then add up to five data sources from the OneLake catalog. Select relevant tables, configure agent instructions with business context, test with sample questions, and publish when ready. The entire process takes minutes.
Can Microsoft Fabric data agents modify or delete my data?
No, Fabric data agents have read-only access by design. They can query and retrieve information but cannot update, delete, or modify any data in your sources. This security restriction is built into the architecture and cannot be overridden, ensuring agents only provide insights without risking data integrity or accidental changes.
