We just concluded Microsoft IQ Deep Dive with Python, a three-part livestream series all about Microsoft IQ.
We showed how to use the four IQs to ground your AI applications and agents:
- Work IQ: user-specific retrieval of M365 data, like Teams chats, emails, and calendar events.
- Fabric IQ: retrieval of data stored in OneLake, via Fabric ontologies, graphs, and data agents.
- Web IQ: real-time web results with super low latency
- Foundry IQ: multi-source agentic retrieval on search indexes plus remote sources (including Web IQ, Work IQ, and Fabric IQ)
The four IQs are all exposed as MCP endpoints, so you can easily integrate into your own agents, or add to your Foundry agents via the Foundry Toolbox. Check out our code samples for Python notebooks and agents that use each of the MCP servers and APIs.
All of the materials from our series are available for you to keep learning from, and linked below:
- Video recordings of each stream
- PowerPoint slides that you can use for reviewing or even teaching the material to your own community
- An annotated write-up of each presentation, so you can quickly read through
šš½āāļø Have follow up questions? Join the weekly Python+AI office hours on Foundry Discord.
Microsoft IQ Deep Dive with Python: Foundry IQ
In the first session, we dived into Foundry IQ (Azure AI Search), exploring how it helps agents and applications work with curated knowledge and organizational context. We built knowledge bases in Python and connected them to multiple knowledge sources, including file knowledge sources, search indexes built from ingested data, and the Web IQ MCP server. Then we performed multi-source agentic retrieval on those knowledge bases, which executes queries in parallel and merges the results with state-of-the-art ranking models. Finally, we built agents in Python using Microsoft Agent Framework and grounded their responses in Foundry IQ results three different ways: a custom tool calling the knowledge base API, the knowledge base MCP endpoint, and a Foundry Toolbox. We deployed those agents to Foundry Agent Service as hosted agents and published one to Teams.
- š¼ļø Slides for this session
- š Write-up for this session
- š» Code repository with examples: iqdeepdive
Microsoft IQ Deep Dive with Python: Work IQ
In the second session, we focused on Work IQ and how it brings workplace context into AI-powered experiences. We compared Work IQ to Microsoft Graph, then explored all three protocols it speaks ā A2A, MCP, and REST ā with runnable Python notebooks for each. We walked through the 10 generic tools that Work IQ exposes over MCP, including ask, which calls Microsoft 365 Copilot directly, and do_action, the only write path. We also connected Work IQ to a Foundry IQ knowledge base as a knowledge source, so a single query returns a blended answer across indexed HR documents and live work context. Then we wired Work IQ into a Microsoft Agent Framework agent as an MCP tool, and finished with Agent 365 autopilots ā agents that get their own Microsoft 365 identity, mailbox, and place in the org chart, and act as themselves rather than on behalf of you. A live demo showed the Work Mate autopilot reading its own mailbox in Teams and emailing a customer directly.
- š¼ļø Slides for this session
- š Write-up for this session
- š» Code repository with examples: iqdeepdive
Microsoft IQ Deep Dive with Python: Fabric IQ
In the final session, we explored Fabric IQ and how it connects AI experiences to structured business data stored in Microsoft Fabric's OneLake. We introduced the key components of Fabric IQ ā ontologies, graphs, semantic models, and data agents ā and showed how each one helps describe, organize, and reason over operational data. Ontologies provide a shared business vocabulary that maps entity types, properties, and relationships to actual OneLake data. Graphs offer dedicated graph database capabilities for queries requiring extensive relationship traversal. Semantic models expose Power BI analytics through DAX measures on star-schema tables. Data agents combine all of these behind a single conversational interface that selects the right source and query language automatically. For each component, we demonstrated the Ontology MCP server and Data Agent MCP server for agent integration, and showed how to add each as a knowledge source to Foundry IQ knowledge bases for multi-source retrieval.
Microsoft