Microsoft Reactor Stockholm

Fine-tuning adapts a model to the language, examples, and behaviors of a specific task, helping developers improve quality, reduce prompt complexity, and right-size performance and cost. Join us as we explore the latest fine-tuning capabilities in Microsoft Foundry and see how customized models can strengthen specialized AI agent experiences.

Adding more agents does not automatically create a better system. Multi-agent solutions need clear responsibilities, controlled execution, recoverable state, and carefully designed decision boundaries.

In this session, we will extend the agent built in Episode 1 into a coordinated incident-management workflow. Specialist agents will handle triage, knowledge retrieval, impact assessment, and remediation planning. We will use Microsoft Agent Framework to demonstrate sequential, concurrent, and handoff orchestration patterns, and discuss where each pattern is appropriate.

The workflow will introduce typed state, checkpointing, failure recovery, and human-in-the-loop approval before any sensitive remediation action can continue. We will also separate deterministic application logic from agentic reasoning, showing which decisions should be handled by an AI agent and which should remain enforced by conventional code.

Attendees will see how a multi-agent workflow behaves when interrupted, how it resumes from saved state, and how human approval can be incorporated without losing execution context.

By the end of the session, attendees will be able to design controlled multi-agent workflows that combine AI judgment with reliable business-process execution. Key topics: Multi-agent orchestration patterns Sequential, concurrent, and handoff workflows Typed workflow state Checkpointing and recovery Human-in-the-loop approvals Agentic versus deterministic steps Failure handling and idempotency.