Build a Knowledge Assistant with Microsoft Foundry — labIT PRO
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Build a Knowledge Assistant with Microsoft Foundry

12 interconnected hands-on labs that take you from an empty Foundry project to a grounded, guarded, evaluated help desk agent published to Microsoft Teams and Microsoft 365 Copilot.

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What's Included
12 Interconnected Labs
Beginner Friendly
Fully Guided
Step-by-Step
No Azure Sub Needed
Lifetime Access
Prepares for AI-900 & AI-102

Lab Project Overview

This interactive lab project guides you through the end-to-end build of an AI help desk assistant in Microsoft Foundry — Microsoft's platform for deploying models, building agents, and running them safely in production.

Starting from the ground up, you'll create a Foundry resource and project, deploy a chat model, and shape its behavior in the playground. Then you'll ground it on Contoso's IT policy documents, save it as an agent, connect a Foundry IQ knowledge base, add web search with citations, and evaluate how well it answers.

Every lab builds on the last, so by the time you're done you'll have a complete, production-ready agent — with guardrails, role-based access, tracing and cost monitoring, and a live publication to Teams and Microsoft 365 Copilot.

Whether you're studying for AI-900 or AI-102 — or just looking to build real Microsoft Foundry skills — this project gives you practical experience that goes far beyond what any video course can offer.

What You'll Learn
Create a Foundry project and deploy a chat model
Ground a model on your own documents and a Foundry IQ knowledge base
Configure guardrails, blocklists, and Foundry roles
Evaluate agent quality with a test dataset and an AI judge
Trace, monitor, and budget an agent with Application Insights
Publish an agent to Microsoft Teams and Microsoft 365 Copilot
Module 3
Extend and Evaluate the Agent

Give the agent reach beyond Contoso's own documents, then measure how well it performs. You'll add web search with citations for questions the knowledge base can't answer, then run an evaluation against a test dataset using contoso-chat as the judge.

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