Skip to content

00 — Prerequisites

Before starting this workshop, make sure you have the tools, access, and Azure resources listed below.

Region support for hosted agents

Hosted agents are only available in specific Azure regions

Choose a supported region when provisioning your resources.

Recommended region: East US 2 or Sweden Central

Hosted agents are currently in public preview and available in these regions:

Americas Europe Asia-Pacific Other
East US 2 Sweden Central Southeast Asia South Africa North
North Central US Norway East Japan East
West US France Central Korea Central
West US 3 Switzerland North South India
Canada Central Poland Central Australia East
Brazil South Spain Central

Source: Hosted agents — Region availability (updated April 2026).

Azure prerequisites

Requirement Details
Azure subscription Create one for free
Role Owner on the subscription (or Contributor + User Access Administrator) — needed to create resources and assign roles

All other Azure resources (Foundry account, project, model deployment, Azure AI Search, ACR) are provisioned by the workshop's Bicep template.

Local prerequisites

Tool Minimum version Install
Python 3.12+ python.org
Azure CLI 2.67+ Install Azure CLI
Azure Developer CLI (azd) 1.24.0+ Install azd

Docker Desktop is not required — azd deploy builds containers remotely.

Step-by-step setup

1. Authenticate

az login
azd auth login

Verify:

az account show --query "{subscription: name, id: id}" -o table

2. Install the azd agent extension

azd ext install azure.ai.agents

Verify the extension is installed (version 0.1.27-preview or later):

azd ext list

3. Clone the repository

git clone https://github.com/beyondelastic/foundry-advanced-workshop.git
cd foundry-advanced-workshop

4. Provision Azure resources

This workshop provisions its own Foundry account, project, model deployment, and container registry using Bicep.

Resource name = subdomain (critical)

The Bicep template uses baseName as both the ARM resource name and the custom subdomain for the Foundry account. This ensures the hosted agent platform can correctly resolve the project endpoint. Do not reuse an existing Foundry account where the resource name differs from the subdomain.

# Create a resource group in a supported region
az group create \
  --name rg-foundry-advanced-workshop \
  --location eastus2

# Deploy all infrastructure
az deployment group create \
  --resource-group rg-foundry-advanced-workshop \
  --template-file infra/main.bicep \
  --parameters baseName=<your-unique-name>

Replace <your-unique-name> with a globally unique string (lowercase, no spaces, e.g. foundryws-janedoe). This becomes both the resource name and the endpoint subdomain.

??? example "Example output" 

{
  "foundryAccountName": "foundryws-janedoe",
  "foundryProjectName": "foundryws-janedoe-project",
  "projectEndpoint": "https://foundryws-janedoe.services.ai.azure.com/api/projects/foundryws-janedoe-project",
  "searchServiceName": "foundryws-janedoe-search",
  "searchConnectionName": "foundryws-janedoe-aisearch",
  "acrLoginServer": "foundrywsjanedoe.azurecr.io",
  "projectPrincipalId": "xxxxxxxx-xxxx-xxxx-xxxx-xxxxxxxxxxxx"
}

Save the outputs — you'll need them in the next step.

5. Assign yourself the Foundry Project Manager role

Export variables from your deployment output so you can reuse them throughout the workshop:

# Set these once — used in all subsequent commands
export BASE_NAME=<your-unique-name>
export RESOURCE_GROUP=rg-foundry-advanced-workshop
export ACCOUNT_ID=$(az cognitiveservices account show \
  --name $BASE_NAME --resource-group $RESOURCE_GROUP --query id -o tsv)

az role assignment create \
  --assignee $(az ad signed-in-user show --query id -o tsv) \
  --role "Foundry Project Manager" \
  --scope "$ACCOUNT_ID"

Why Foundry Project Manager?

This role includes both data-plane permissions to create agents and the ability to assign the Foundry User role to the agent identity that the platform creates at deploy time.

6. Create a virtual environment

python -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt

7. Configure environment variables

cp .env.example .env

Open .env and fill in from your deployment outputs:

Variable Value from Bicep output
AZURE_AI_PROJECT_ENDPOINT projectEndpoint output
AZURE_AI_MODEL_DEPLOYMENT_NAME The model name (e.g. gpt-4.1-mini)

8. Start the workshop UI

mkdocs serve

Open http://127.0.0.1:8000.

Verification checklist

  • [ ] az account show displays the correct subscription
  • [ ] azd ext list shows azure.ai.agents at version 0.1.27-preview or later
  • [ ] Python 3.12+ is active in your virtual environment (python --version)
  • [ ] az deployment group show confirms your infra deployed successfully
  • [ ] .env file contains your AZURE_AI_PROJECT_ENDPOINT and AZURE_AI_MODEL_DEPLOYMENT_NAME
  • [ ] Your Foundry resource is in a supported region
  • [ ] mkdocs serve runs and the workshop site loads at localhost:8000

Official references