什么是Data Agent？

Towards Data Science

Towards Data Science2026年5月26日

什么是Data Agent？

2.0Score

TL;DR · AI 摘要

本文介绍了Data Agent的概念及其在现代软件开发中的应用。

核心要点

Data Agent是一种用于自动化数据操作的工具。
它可以提高数据处理效率。
适用于各种规模的企业。

结构提纲

按章节快速跳转。

§引言
简述Data Agent的基本概念和作用。
·核心机制
详细解释Data Agent的工作原理。
›数据获取
说明Data Agent如何从多个源获取数据。
›数据处理
分析Data Agent如何对数据进行清洗和转换。
·应用场景
讨论Data Agent在不同场景下的应用。
›企业级应用
举例说明Data Agent在大型企业的应用案例。
›中小型企业应用
举例说明Data Agent在中小型企业的应用案例。

思维导图

用一张图看清主题之间的关系。

查看大纲文本（无障碍 / 无 JS 友好）

Cookie介绍
- Cookie分类
  - 必要Cookie
  - 分析Cookie
- Cookie管理
  - 拒绝所有Cookie
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金句 / Highlights

值得收藏与分享的关键句。

Data Agent是一种用于自动化数据操作的工具。
— 第 2 段
⬇︎ 下载 PNG 𝕏 分享到 X
它可以提高数据处理效率。
— 第 2 段
⬇︎ 下载 PNG 𝕏 分享到 X
适用于各种规模的企业。
— 第 2 段
⬇︎ 下载 PNG 𝕏 分享到 X

#Data Agent#自动化#数据处理

打开原文

What Is a Data Agent? | Towards Data Science

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Agentic AI

What Is a Data Agent?

A simple explanation of what a data agent is and how it works

Marina Tosic

May 26, 2026

5 min read

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Photo by Kelly Sikkema on Unsplash

Working at Microsoft, I have the opportunity to try new AI-powered analytical tools, including Microsoft Fabric’s data agent. That’s why I want to share what I’ve learned, explain what a data agent is, and highlight the difference between it and a “standard” AI agent.

So, without further ado, here is my definition of a data agent:

A data agent is a report you can talk to.

For those of us in analytics, this means two long-held wishes might finally become a reality:

#1: Analysts spend way less time building visualisations.

#2: Self-service insights come closer to business users.

Let me elaborate on each of these points a bit more.

Fewer visualisations, not fewer insights

I really enjoy a good report that can tell me “what’s up” with the metrics I am currently interested in. But being trained in analytics, I know how reports can sometimes cast metrics in the wrong light, leading business users to frequently ask analysts for KPIs interpretation, usually 10 minutes before important meetings.

And that’s one of the reasons we often end up in a vicious cycle of having dashboards no one is using, and stakeholders constantly wanting “the number” served ad hoc or via spreadsheets.

On the bright side, visualisations and spreadsheets are not going anywhere, but serving the insights has a new way with a Fabric data agent.

Instead of wrapping queries in graphs, you can wrap them in prompts and instructions paired with the consumption-ready governed data estate in Fabric, i.e., in a lakehouse, warehouse, Power BI semantic models, KQL database, or even an ontology. This implies the underlying data still needs to be prepared and modelled to answer business questions such as “_What was the revenue this week compared to last week?_”

However, from a design perspective, rather than creating a scoped visual report to answer this business question, you now create a scoped data agent to provide this, and other subsets of answers derived from the underlying data model(s).

More precisely, the input-output flow goes as follows:

(1) a stakeholder asks a question, (2) the agent, powered by Azure OpenAI Assistant API, interprets the question and “decides” which of data sources is most likely to have the answer based on source schemas and agent instructions, (3) generates the appropriate query (SQL, DAX, or KQL depending on the source type), (4) validates it, (5) executes it under the stakeholder’s credentials, and (6) returns the result as a text or a table, not (yet) as a visual.

In sum, a stakeholder interaction with insights via the data agent is a Q&A session on top of the curated dataset, and drill-down visuals can be replaced with follow-up questions, such as “_Can you also break the revenue out by segment?_”

With that, it is clear how analysts’ work no longer needs to be re-expressed only via dashboards, aka the long-known tangible proof that the work of capturing the business logic within data models was delivered.

Now, let’s talk about…

Self-service insights, closer to where business users“live”

I mentioned before that reports can sometimes misrepresent metrics, but that’s not the only reason why “_If you build it, they will come_” rarely works for them or analytics in general. The truth is, the knowledge barrier is often too high to understand the underlying semantic models and how to use BI tools to create visuals on top.

Although this points to data literacy, which is a change-management problem, it’s a fact that the targeted business audience, who should be report consumers, often has too much on their plate to bother learning BI tools for self-service analytics.

That’s why it’s important to bring insights closer to where end users “live”, which nowadays points towards AI-powered tools like M365 Copilot.

With the possibility to expose insights via data agents outside of Fabric, analysts can now focus on the analytical logic behind self-service data agents, and end users can access insights in the same AI-powered tools that support their other daily tasks, without the complexity of switching to another platform.

I have to note this is not the only way to integrate Fabric data agents in the workflows, and regardless of whether you’re a developer or a consumer, it’s good to know…

The difference between data and an AI agent

Photo by Dynamic Wang onUnsplash

We’ve learned so far that the Fabric data agent is an analytical agent focused on read-only, governed data access, capable of translating natural language prompts into complex database queries that unlock insights, even outside the Fabric tenant.

On the other side, an AI agent is defined as a system that allows Large Language Models (LLMs) to _do things,_ not just respond to prompts, on behalf of users or other systems by accessing tools and knowledge.

Meaning, the whole magic is in the AI agent setup, where you can use a Fabric data agent as a specialised tool or knowledge source.

I’ll illustrate this with one simple example.

Imagine an authorised user requests the AI agent to _“Draft an email to the team summarising last week’s revenue by segment.”_ To get this work done, the AI agent would, among other things, need to prepare revenue insights from the enterprise database. So, in an aim to reduce errors in revenue calculation, the developer would design an agentic workflow to route the input prompt to the Fabric data agent _tool_, which would handle the heavy lifting of determining the schema, writing the query, executing it, and returning the precise figures. Finally, the AI agent would then use those figures to finish its broader workflow and write the email.

What’s the difference between those two, then? It’s that an AI agent _acts_, while the data agent _grounds_.

* *

_Thank you for reading._
_If you found this post valuable, feel free to share it with your network._
_Connect for more stories on__Medium__ and__LinkedIn__._