What happens when you let AI agents do the building? In this hands-on lab, you'll go from an empty terminal to a deployed app on Azure — with GitHub Copilot CLI and coding agents handling the scaffolding, coding, debugging, and deployment. You'll use the new Azure skills to provision resources and wire up services through natural language, no portal required. This isn't a demo you watch. You'll walk out with a real, working dev workflow you can take straight to your next project.

Across 75 minutes (Level 300), you'll ship two services from a single Copilot prompt — a Python Flask LEGO set browser on Azure Container Apps and a Python Azure Function App that batch-upserts LEGO sets into Azure Cosmos DB — then put on your architect hat and evaluate the AI's decisions: review the generated Bicep, harden the deployment with managed identity + RBAC, break it on purpose, and run a full forensic investigation with KQL — all from GitHub Copilot CLI.

To get started in a guided lab session:

• Sign in to your Skillable lab VM and start Docker Desktop
• Open PowerShell and run az login, azd auth login, then copilot — follow Login & Launch to install the Azure Skills plugin (/plugin install azure@azure-skills)
• Follow the Lab Overview and work through the four scenarios in order

If you're following these steps at your own pace:

• Clone this repository: git clone https://github.com/microsoft/Build26-LAB501.git
• Install the prerequisites: Python 3.13+, Docker Desktop, Git, Azure CLI (with Bicep), Azure Developer CLI (azd), GitHub Copilot CLI, and an Azure subscription with Contributor access
• Provision your own Azure Cosmos DB with the LEGO dataset (database LegoDatabase, container legoSets), then start at Set Up the Starter App and work through the four scenarios

By the end of this session, you will be able to:

• Chain Azure skills (azure-prepare → azure-validate → azure-deploy) from a single natural-language prompt in GitHub Copilot CLI to scaffold IaC, Docker, and config and ship a containerized app to Azure
• Critically review AI-generated Bicep, Dockerfiles, and architecture diagrams — spot the production gaps and use azure-rbac and azure-resource-visualizer to close them with least-privilege managed identity and accurate documentation
• Triage and operationalize production issues with azure-diagnostics — follow its reasoning chain from system logs to root cause, then turn the post-mortem into KQL queries and alert rules without ever opening the Azure Portal

Try these prompts with GitHub Copilot to explore the topics from this session. Open Copilot Chat in VS Code (Ctrl+Alt+I on Windows/Linux, Cmd+Shift+I on Mac), paste a prompt, and see what you learn. Try connecting the Microsoft Learn MCP Server for the latest official documentation.

Use these as a starting point — or write your own!

• "Scaffold a Python Flask app on Azure Container Apps and a Python Azure Function App with Bicep and azd, using managed identity for ACR pulls and Cosmos DB access."
• "Review my Container App Bicep for production-readiness gaps — managed identity, RBAC, VNet integration, diagnostic settings, and health probes — and propose fixes."
• "Find the minimum-privilege Cosmos DB RBAC role for an app that only reads data, and generate the az cosmosdb sql role assignment create command."
• "Visualize the resources in my resource group as a Mermaid architecture diagram, including cross-resource-group dependencies like Cosmos DB."
• "My Container App is returning 503. Pull system logs, correlate with ingress configuration, and tell me the root cause and fix."
• "Write a KQL query against ContainerAppSystemLogs_CL that calculates downtime between the first ProbeFailed event and the next RevisionReady event, then turn it into an az monitor scheduled-query create alert rule."

1. GitHub Copilot CLI + Azure Skills plugin (azure-prepare, azure-validate, azure-deploy, azure-rbac, azure-resource-visualizer, azure-diagnostics) and the Azure MCP Server
2. Azure Container Apps, Azure Functions (Python, Flex Consumption) for batch LEGO set ingestion, Azure Container Registry, and Azure Cosmos DB (NoSQL) with managed identity + RBAC
3. Azure Developer CLI (azd), Bicep, Docker, Python 3.13 / Flask, Azure Monitor + Log Analytics, and KQL

The Microsoft Learn MCP Server is a remote MCP Server that enables clients like GitHub Copilot and other AI agents to bring trusted and up-to-date information directly from Microsoft's official documentation. Get started by using the one-click button above for VSCode or access the mcp.json file included in this repo.

For more information, setup instructions for other dev clients, and to post comments and questions, visit our Learn MCP Server GitHub repo at https://github.com/MicrosoftDocs/MCP. Find other MCP Servers to connect your agent to at https://mcp.azure.com.

Note: When you use the Learn MCP Server, you agree with Microsoft Learn and Microsoft API Terms of Use.

Yun Jung Choi 📢

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