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@Dave R - Microsoft Azure & AI MVP☁️
如何在 Microsoft Fabric 中将数据工程委派给 AI Agent
文章给出两种在 Microsoft Fabric 中委派数据工程任务的实用模式，涉及 Fabric 感知的 Copilot、有边界的 Agent、OneLake、Spark 与 CI/CD，并主张将架构与合并决策保留在人工控制之下。
AI推荐理由 面向 Microsoft Fabric 的实操文章，给出两种将数据工程委派给 AI Agent 的模式，涉及 Copilot、OneLake、Spark 与 CI/CD，并强调架构与合并决策保留人工控制，对数据工程落地有具体参考价值；但为个人经验分享，无独立验证。
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Microsoft Fabric
Data Engineering
Github Copilot
Apache Spark
Artificial Intelligence
How to Delegate Data Engineering to AI Agents in Microsoft Fabric
Two practical patterns for Fabric-aware Copilot, bounded agents, OneLake, Spark, and CI/CD, with architecture and merge decisions kept in human hands.
Dave R - Microsoft Azure & AI MVP☁️
10 min read
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Sep 14, 2026
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How to Delegate Data Engineering to AI Agents in Microsoft Fabric
Code generation is the easy part. The hard part is delegation.
If an AI agent is going to touch a real notebook, inspect real schemas, or change a large Spark codebase, the important question is not whether it can generate code. It is whether you can give it enough context and enough boundaries to make the result reviewable, testable, and safe to merge.
We will review two very different workflows: a Fabric-aware Copilot flow that turns Eventstream data into a bronze-to-silver pipeline, and an autonomous development loop that runs inside a controlled local environment before code is promoted to Fabric Spark.
The common pattern is simple: the engineer owns architecture and quality; the agent gets a bounded implementation problem.
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The role shifts from typing every transformation to defining architecture, constraints, tests, and review gates.
Pattern 1: let Copilot plan the streaming pipeline, then…