Microsoft Acquires Osmos to Bring Agentic Data Engineering to Azure Fabric

TL;DR: Microsoft acquired Osmos, an agentic AI data engineering platform , and will integrate it into Azure Fabric. For .NET and Azure developers, this means AI agents will soon automate tedious data prep workflows—letting you spend less time wrangling CSV files and more time shipping features.

The Problem Osmos Solves

Organizations face a common challenge: data is everywhere, but making it actionable is often manual, slow and expensive. Many teams spend most of their time preparing data instead of analyzing it. If you’ve ever spent a sprint just cleaning, deduplicating, and transforming raw data, you’ve lived this pain. Osmos solves this problem by applying agentic AI to turn raw data into analytics and AI-ready assets in OneLake, the unified data lake at the core of Microsoft Fabric. Translation: instead of writing SSIS packages or hand-crafted ETL scripts, you describe what you want, and an AI agent figures out the transformations.

What This Means for Your Azure Stack

For .NET developers building on Azure, the implications are straightforward:

Less boilerplate, more iteration. With the acquisition of Osmos, Microsoft is taking the next step toward a future where autonomous AI agents work alongside people — helping reduce operational overhead and making it easier for customers to connect, prepare, analyze and share data across the organization.
Native integration with Fabric. Osmos will land inside Azure Fabric, so if you’re already using Power BI, Azure Data Factory, or Lakehouse, the agent will become part of your existing workflow—no new vendor to manage.
Agentic AI becomes practical. Anthropic’s Model Context Protocol (MCP), a “USB-C for AI” that lets AI agents talk to the external tools like databases, search engines, and APIs, proved the missing connective tissue and is quickly becoming the standard. OpenAI and Microsoft have publicly embraced MCP , so expect Osmos to speak MCP fluently when it lands in Fabric.

Real-World Scenario

Imagine you’re building a .NET Core API that ingests customer transaction data from multiple legacy systems. Today, you’d:

// Manual ETL orchestration (the old way)
var rawData = await FetchFromLegacyERP();
var cleaned = CleanDuplicates(rawData);
var transformed = NormalizeSchemas(cleaned);
await LoadToLakehouse(transformed);

Soon, with Osmos in Fabric, you’ll describe the intent to an agent:

"Ingest ERP transactions, deduplicate by order ID, 
map legacy schema to our standard, and land in OneLake. 
Flag any rows with missing customer IDs for review."

The agent explores your data, suggests transformations, and you review and approve the changes—keeping humans in the loop where it matters.

The Bigger Picture

In successful modernization efforts, agentic AI does not replace engineers. It changes how they spend their time. Agents explore, correlate, and propose. Humans decide, review, and take responsibility. The control plane stays human, especially in mission-critical systems where correctness, compliance, and trust matter more than speed. This is the pragmatic 2026 approach: AI as a multiplier, not a replacement. Osmos in Fabric embodies that philosophy.

What You Should Do Now

If you use Azure Fabric or Power BI: Watch the Microsoft Fabric Blog for Osmos integration timelines and early access programs.
If you’re building data pipelines in .NET: Start thinking about where agentic AI could accelerate your workflows—data prep is the obvious first target.
If you’re on legacy ETL: This is a good moment to evaluate a Fabric migration. The ROI just got better.