Build 2026 in 15 Minutes

Key Concepts

• Foundry IQ: A platform for curated institutional knowledge management.
• Fabric OneLake: A data virtualization layer for enterprise data.
• Work IQ: An AI-powered engine for personalization and understanding work relationships.
• Web IQ: An agent-optimized search layer built on the Bing global index.
• MAI (Microsoft AI) Models: A diverse suite of specialized models (Instruct, Plan, Thinking, Image, Transcribe, Voice).
• Agentic Governance: Frameworks (M-assert, Agent Control Specification) for securing and evaluating AI agents.
• Majorana 2: A quantum computing architecture achieving a 1,000x increase in qubit stability.

---

1. Intelligence and Data Infrastructure

The core theme of the event was "making AI real for organizations" by focusing on trust, safeguards, and high-quality intelligence.

• Foundry IQ: Now GA, this tool curates institutional knowledge (contracts, policies) and optimizes token usage by sending only the most relevant data segments to the model.
• Fabric Integration: Supports OneLake catalogs (unstructured/semi-structured data) and Fabric IQ (semantic models). It utilizes data virtualization to connect to Azure SQL, MCP servers, Salesforce, and ServiceNow.
• Work IQ: Launching June 16, 2026, this engine maps the relationships between work artifacts and user behavior. It powers Copilot experiences and allows third-party agents to access enterprise-specific personalization.

2. Optimized AI Models and Hybrid Execution

Microsoft is moving away from a "one-size-fits-all" model approach toward a diverse, token-efficient ecosystem.

• MAI Models:
  • Ailon 1.0 Instruct/Plan: Small language models (SLMs) designed for local execution on Windows devices.
  • MAI Thinking 1: A 35B parameter "mixture of experts" model trained on clean, licensed data.
  • MAI Image 2.5 & Transcribe 1.5: Specialized models for image editing and high-speed speech-to-text.
• Hybrid AI: The Microsoft Execution Container (MXC) allows for secure, isolated agent execution across Windows, Linux, and macOS. RTX Spark is utilized to enhance local execution for larger models.
• Frontier Tuning: Uses reinforcement learning to tune models specifically to a company’s workload and data, rather than just relying on prompt engineering.

3. Agentic Security and Governance

To ensure trust, Microsoft introduced a robust framework for monitoring and controlling AI agents.

• M-assert (Multi-model Agentic Scanning Harness): A system of over 100 specialized agents that discover, debate, and prove exploitable bugs in code. It is integrated with Defender and GitHub code security.
• Agent Control Specification: Provides "interception points" (startup, input, model calls, tool calls, output) where administrators can apply policies to lock down agent behavior.
• Rubik Evaluator: Automatically generates test cases based on agent specifications to ensure the system adheres to desired behaviors.

4. Agent Runtime and Distribution

• Foundry Agents: Acts as the runtime for agents, supporting both no-code (prompt-based) and hosted (container/code-based) deployments. It enables "Agent-to-Agent" (A2A) communication.
• Microsoft Scout: Described as the "first autopilot," this is an always-on personal agent that acts on the user's behalf across M365, managing tasks like scheduling conflicts and expense reporting.
• Fabric Rayfin: A managed backend-as-a-service that allows developers to deploy apps via GitHub workflows directly into Fabric, inheriting enterprise-grade security and OneLake data access.

5. Quantum Computing Breakthrough

• Majorana 2: A significant advancement in quantum hardware. By improving qubit stability from milliseconds to 20 seconds (a 1,000x increase), Microsoft is moving toward a scalable quantum processor.
• Timeline: The company projects a fully usable, large-scale quantum computer by 2029, capable of supporting up to a million qubits.

---

Synthesis and Conclusion

The overarching strategy presented is the transition from "flashy" AI features to a robust, enterprise-grade "Agentic Economy." By providing a unified data layer (Fabric/OneLake), a diverse model library (MAI), and strict governance frameworks (M-assert/Agent Control), Microsoft aims to solve the "trust" problem in AI. The shift toward local, hybrid, and agent-based execution—exemplified by Microsoft Scout—suggests that the future of work will be defined by autonomous agents that understand enterprise context, operate within strict security boundaries, and utilize specialized, token-efficient models.