Microsoft Fabric

The unified data platform where every workload reads from and writes to OneLake. No data movement between engines. Fabric's AI layer builds on top of that unified foundation - the Data Agent, Eventhouse, and Ontology all operate directly on data where it already lives.

When to choose Fabric over Foundry: data already lives in Fabric or OneLake, users need NL query over Fabric data, or you need AI over real-time streaming data.

Architecture

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OneLake

The storage foundation. Every Fabric workload - Lakehouse, Warehouse, Eventhouse, Power BI - reads from and writes to OneLake. You don't move data between engines; every engine uses the same copy.

# AI agent accessing OneLake via MCP
agent = client.agents.create(
    model="gpt-5",
    name="data-agent",
    tools=[
        {"type": "mcp", "server_url": "https://my-fabric.fabric.microsoft.com/mcp"}
        # Agent can browse workspaces, read Delta files, discover table schemas
    ]
)

OneLake for Microsoft Foundry | OneLake MCP

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Eventhouse

High-performance time-series and streaming data store. The storage layer for Real-Time Intelligence.

Eventhouse is the right store when you need sub-second query latency over streaming or time-series data. It's not a replacement for the Lakehouse - it's for real-time data that arrives continuously.

AI agents for Eventhouse

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Operations Agents

AI agents that monitor real-time data in Eventhouse and act autonomously. Replace dashboard-watching with continuous automated monitoring.

How it works:

1. Define what to monitor (metric threshold, anomaly condition, pattern)
2. Operations Agent queries Eventhouse continuously
3. When condition is met - notify, trigger Power Automate, create ticket, call API
4. No user trigger required

Example: IoT sensor anomaly in Eventhouse - Operations Agent detects it - triggers Power Automate - creates ServiceNow incident - notifies engineer in Teams.

Operations Agents docs

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Fabric Data Agent

Natural language query across all Fabric data - lakehouses, warehouses, Eventhouses, semantic models - through a single conversational interface.

Key differentiator: auto-selects the query language. Ask a question; the Data Agent decides whether to use SQL (warehouse), KQL (Eventhouse), or DAX (semantic model) based on what the data is.

User: "What were sales last quarter, and what's the current pipeline status?"

Data Agent:
- Routes "sales last quarter" → SQL query against Data Warehouse
- Routes "current pipeline status" → KQL query against Eventhouse
- Combines results → grounded answer

Within multi-agent architectures, the Data Agent serves as the conversational analytics component - other agents delegate data questions to it.

Data Agent docs

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Ontology

A shared, governed business model that gives consistent meaning to entities, metrics, and KPIs across all agents and tools in Fabric.

Without Ontology: one agent defines "churn" as 90-day no-purchase, another defines it as subscription cancellation. Both pull different numbers. Reports and agents disagree.

With Ontology: "churn" is defined once, governed centrally, consumed by the Data Agent, Fabric IQ, Copilot Studio agents, and Power BI identically.

Ontology docs

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Fabric IQ

The intelligence layer that connects Fabric data to the broader Microsoft AI ecosystem.

Fabric IQ is what makes Fabric data visible to agents that live outside Fabric. Without it, every integration is custom.

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Fabric AI Services Summary

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When Fabric vs Foundry

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Getting Started

Key Takeaways

• OneLake is the only copy of the data. Every Fabric workload reads from it directly - no ETL between engines. This makes Fabric significantly cheaper to operate than multi-platform stacks at scale.
• Eventhouse is not a Lakehouse replacement. Use it for real-time and time-series data that arrives continuously. Use the Lakehouse for historical batch data.
• Fabric Data Agent handles the query language decision - SQL, KQL, or DAX - based on context. This is the key differentiator over building your own text-to-SQL pipeline.
• Ontology solves the metric consistency problem that kills multi-agent accuracy. Define "churn" once; every agent and report uses the same definition.
• Fabric IQ is the integration point between Fabric and the rest of Microsoft AI. Without it, every Foundry or Copilot Studio agent that needs Fabric data requires a custom connector.
• Operations Agents replace dashboard-watching. The ROI case is straightforward: continuous monitoring at near-zero marginal cost vs. human monitoring shifts.