Microsoft IQ Overview
John Savill explains Microsoft IQ as a way to connect AI agents to the most relevant intelligence sources (organizational knowledge, internet information, and model capabilities) so agents can complete tasks more effectively.
Overview
What “intelligence” an agent needs
The video frames agent work around:
- Responsibilities and requirements: what the agent is expected to do and the constraints it must operate under.
- Tasks and AI: mapping tasks to AI-assisted execution.
- Sources of intelligence:
- Institutional knowledge (enterprise/internal data)
- Internet (external/public information)
- AI and models (the model layer that can reason, summarize, generate, and plan)
Models: flexibility and using multiple models
Savill discusses the idea that:
- Different tasks may benefit from different models.
- A solution should support multiple models and flexibility, rather than assuming a single model is always best.
Bringing intelligence and tools to AI
The core theme is connecting:
- Relevant data (internal + external)
- Tools (capabilities an agent can invoke)
- Models (one or more LLMs)
…so an agent can act on tasks with the right context.
Microsoft IQ and its “IQ” surfaces
The video introduces Microsoft IQ and breaks it down into multiple areas:
- Work IQ
- Fabric IQ
- Foundry IQ
- Web IQ
Savill positions these as different ways Microsoft IQ can surface or apply intelligence depending on where the work is happening (work context, data/analytics context, AI platform context, and web context).
Relevance: getting the most relevant data
A recurring point is selecting and using:
- The most relevant data for the task at hand
- All types of intelligence (enterprise + web + model capabilities)
Identity, least privilege, and security
Savill calls out security requirements when agents are connected to data and tools:
- Identity and least privilege: agents should only have the permissions they need.
- Security and protection: controls to protect data and reduce risk when agents can access multiple sources and invoke tools.
Observe and improve
The video closes with the idea of:
- Observing how agents behave and what they access
- Improving the system over time based on what’s learned