What Is a Data Agent?

2026-05-26 · Source: Towards Data Science · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics · Depth: Intermediate, short

Summary

Microsoft Fabric's data agent is introduced as a conversational report, allowing users to interact with data through natural language Q&A. This innovation aims to significantly reduce the time analysts spend creating visualizations and bring self-service insights closer to business users. The agent, powered by Azure OpenAI Assistant API, interprets questions, identifies relevant data sources like lakehouses or Power BI semantic models, generates and validates appropriate queries (SQL, DAX, KQL), executes them under user credentials, and returns results as text or tables. Unlike a standard AI agent that "acts" on behalf of users, a data agent "grounds" by providing precise, governed data access, serving as a specialized tool within broader AI agent workflows, such as drafting emails with specific revenue figures.

Key takeaway

For Data Analysts and AI Engineers focused on delivering timely business insights, consider adopting Microsoft Fabric data agents. This shifts your approach from building static dashboards to designing conversational data access, enabling business users to query governed data directly within their daily AI-powered tools like M365 Copilot. Implementing data agents can significantly enhance self-service analytics capabilities and free up valuable analyst time for more complex analytical tasks.

Key insights

Data agents transform data interaction from visual reports to natural language Q&A on governed datasets, streamlining insight delivery.

Principles

• Data agents enable natural language Q&A on curated data.
• They reduce manual visualization creation for analysts.
• Insights can be delivered directly within user workflows.

Method

The data agent workflow involves a stakeholder asking a question, the agent interpreting it, selecting data sources, generating/validating/executing queries (SQL, DAX, KQL), and returning text or table results.

In practice

• Design scoped data agents to answer specific business questions.
• Integrate data agents with M365 Copilot for broader user access.
• Utilize data agents as precise data-grounding tools within AI agent workflows.

Topics

• Data Agents
• Microsoft Fabric
• Self-service Analytics
• Conversational AI
• Azure OpenAI
• Data Governance
• Business Intelligence

Best for: AI Architect, AI Product Manager, Data Analyst, AI Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Towards Data Science.