Microsoft IQ Explained as four context layers
Founder | CEO @ RADACAD | Coach | Power BI Consultant | Author | Speaker | Regional Director | MVP
Reza Rad explains Microsoft IQ as four context layers that help AI agents understand work, business data, knowledge, and the web.
Key insights
- Microsoft IQ is presented as a step toward an AI workforce in organizations.
- The video says AI has moved from chat, to doing work, to becoming autonomous.
- Context engineering is described as the reason Microsoft IQ is possible.
- The four workloads are Work IQ, Foundry IQ, Fabric IQ, and Web IQ.
- Each workload provides a different kind of context and they connect to each other.
Reza Rad (RADACAD) explains Microsoft IQ in a recent YouTube video and frames it as a step toward bringing an AI workforce into an organization. His main point is simple: AI agents need the right context if they are going to do real work, not just answer prompts. The video walks through how AI has evolved and then breaks Microsoft IQ into four workloads: Work IQ, Foundry IQ, Fabric IQ, and Web IQ.
The discussion is aimed at data and AI professionals, Power BI and Fabric developers, architects, and business leaders who want a clear picture of what these IQ names mean. Rather than treating Microsoft IQ as a single feature, the video presents it as a set of connected context layers. That framing matters because it shifts the conversation from model quality alone to the information an agent can actually use.
What's new
The new idea in the video is Microsoft IQ as an approach to context for AI agents. Reza Rad describes a progression from chat agents, to AI that does the work, to AI that becomes autonomous. Microsoft IQ sits in that progression as the context layer that makes more advanced work possible.
He says Microsoft IQ is built from four workloads. Work IQ covers the Microsoft 365 side of how people work. Foundry IQ is the knowledge layer for agents built in Microsoft Foundry. Fabric IQ provides the business data context from Microsoft Fabric. Web IQ brings in context from outside the organization, from the web.
Why it matters
This is most relevant for people planning or building AI agents around Microsoft data and business processes. For admins and architects, the video gives a useful mental model for why an agent needs more than a smart model. For makers and developers, it shows that the quality of an answer depends on whether the agent can reach the right workplace, data, knowledge, and external context.
The video also speaks directly to Power BI and Fabric developers who are trying to place Fabric IQ in the bigger picture. If you already work with Power BI, the takeaway is that business data context is one piece of the agent story, not the whole story. The same applies to teams that rely on Microsoft 365 content and collaboration history for daily work.
For business leaders, the value is in understanding that an AI workforce needs context that spans people, knowledge, business meaning, and current external information. The video does not position Microsoft IQ as a finished end-user app. Instead, it presents it as a set of building blocks for organizations that want agents to do useful work with less guesswork.
How it works
Reza Rad explains Microsoft IQ through context engineering. In the video, context engineering means giving an AI agent the information it needs to understand who people are, what the knowledge is, what the business data means, and what is happening outside the company. He says a smart model alone is not enough.
The four workloads are described like this:
- Work IQ gives the context of how people work in the organization, on the Microsoft 365 side.
- Foundry IQ gives agents in Microsoft Foundry the knowledge layer they need.
- Fabric IQ gives the business data context from Microsoft Fabric so AI understands business meaning, not just tables.
- Web IQ gives context from the web outside the organization.
The video says these workloads connect to each other to give an AI workforce the full picture. That matters because a real work task often spans multiple information types. An agent may need to know who is involved, what internal knowledge applies, how the business data should be read, and whether external conditions have changed. Microsoft IQ is presented as the way those pieces are combined.
Limitations and open questions
The video stays at the concept level. It explains what Microsoft IQ is meant to do, but it does not provide implementation steps, licensing details, rollout information, or a product roadmap. It also does not show a live demo of the workloads working together.
Another open point is scope. The video clearly names the four workloads, but it does not explain how Microsoft will package them, what is available today, or which tenant, region, or license requirements apply. The material also does not spell out where Foundry IQ and Fabric IQ begin and end in a technical sense beyond the high-level descriptions.
Sources
Frequently asked questions
What is Microsoft IQ in this video?
Reza Rad describes Microsoft IQ as a set of four workloads that give AI agents the context they need to do real work in an organization.
What are the four workloads?
The video names Work IQ, Foundry IQ, Fabric IQ, and Web IQ. Each one provides a different kind of context.
Why does context engineering matter?
The video says an AI agent is only as good as the context it gets. Context engineering is how an organization supplies the knowledge, data meaning, and business understanding agents need.
Does the video explain licensing or availability?
No. The material explains the concept and the four workloads, but it does not provide licensing, rollout, or availability details.