Promptflow

name: maf-online-endpoint description: "Deploy a Microsoft Agent Framework (MAF) workflow as a managed online endpoint to an Azure ML workspace or an Azure AI Foundry hub-based project. Wraps any workflow into an init()/run() scoring script, creates conda environment, endpoint and deployment YAMLs, deploy script, and assigns RBAC. Supports managed identity auth and Application Insights tracing. WHEN: deploy MAF workflow, deploy agent-framework workflow, create online endpoint for MAF, deploy workflow to AML, deploy workflow to Foundry project, managed online endpoint for agent workflow, wrap workflow in scoring script, deploy agent as endpoint, realtime endpoint in Foundry project."

Deploy MAF Workflow as a Managed Online Endpoint

This skill wraps a Microsoft Agent Framework (agent-framework) workflow into a managed online endpoint using the standard scoring-script pattern (init() / run()), following the patterns from the azureml-examples managed endpoint samples.

The endpoint can be deployed to either:

Both targets produce the same generated files (online-deployment/ directory) and use the same az ml CLI / azure-ai-ml Python SDK. The difference is in how the workspace is identified and RBAC scope.

Overview

The deployment creates all files in an online-deployment/ subdirectory under the project root:

<project-root>/
  workflow.py              ← the MAF workflow
  online-deployment/
    score.py               ← scoring script
    conda.yml              ← conda environment
    endpoint.yml           ← endpoint config
    deployment.yml         ← deployment template (${VAR} placeholders)
    deploy.sh              ← deploy script (Bash; see notes for Windows)
    .gitignore             ← ignores rendered YAML with secrets

1. Scoring script (score.py) — init() imports the workflow factory; run() creates a fresh workflow instance per request to avoid concurrency errors.
2. Conda environment (conda.yml) — Python 3.11 with agent-framework and azureml-inference-server-http.
3. Endpoint YAML (endpoint.yml) — endpoint name and auth mode.
4. Deployment YAML (deployment.yml) — template with ${VAR} placeholders for environment variables, instance config, and request settings.
5. Deploy script (deploy.sh) — renders the template, creates the endpoint and deployment, runs a smoke test.

Path resolution rule: AML CLI resolves conda_file, code, and scoring_script paths relative to the YAML file location, not the CWD. Since the YAML is inside online-deployment/, use conda_file: conda.yml (same directory) and code: .. (parent = project root).

Agent Interaction Pattern

When the user asks to deploy a MAF workflow as an online endpoint:

1. Ask for the deployment target using vscode_askQuestions:
   • Azure Machine Learning workspace — standalone AML workspace
   • Azure AI Foundry project — hub-based project (the project name is the workspace name)
2. Ask for infrastructure variables (Step 0 §A) using vscode_askQuestions: subscription, resource group, workspace/project name, and the workflow file path.
3. Read the workflow file. Inspect imports and os.environ/os.getenv calls to discover what the workflow needs (Step 0 §B).
4. Ask the user to provide values for any workflow-specific variables that have no defaults.
5. Generate the files from the templates in ./assets/ into an online-deployment/ subdirectory under the project root.
6. Run deployment commands via terminal. On Windows, run az CLI commands directly in PowerShell (the Bash deploy.sh won't work). On Linux/macOS, use deploy.sh or run the commands directly.
7. Assign RBAC (Step 6) — only needed for managed-identity workflows (Foundry/DefaultAzureCredential). Skip for API-key workflows. For Foundry hub-based projects, scope the role assignment to the hub's AI Services resource.
8. Wait 5–10 minutes for RBAC propagation (if applicable), then run smoke test.
9. Report the scoring URI and remind user to .gitignore rendered YAML files that contain secrets.

Step 0 — Gather Required Information

A. Online Endpoint Infrastructure (always required)

The same variables apply to both deployment targets. For a Foundry hub-based project, WORKSPACE_NAME is the AI project name (not the hub name).

Foundry project note: An AI Foundry hub-based project is backed by an AML workspace. All az ml commands and the MLClient SDK work the same way — just use the project name as --workspace-name. The endpoint scoring URI format is identical: https://<endpoint-name>.<region>.inference.ml.azure.com/score

B. Workflow Requirements (depends on the workflow)

Read the user's workflow file and inspect:

1. Imports — determine pip packages for conda.yml.
2. os.environ[...] / os.getenv(...) calls — determine environment variables the deployment must inject.
3. Credential usage — DefaultAzureCredential / ManagedIdentityCredential means RBAC must be set up; an API key means a secret env var.

Common workflow patterns

C. RBAC (after endpoint is created)

Get endpoint managed identity principal ID:

az ml online-endpoint show --name <endpoint> --query identity.principal_id -o tsv

D. Optional (cross-cutting)

Step 1 — Generate the Scoring Script

Use the template at ./assets/score.py.

Key decisions:

• AgentResponse is not JSON-serializable → extract .text before returning.
• project_root = Path(__file__).resolve().parents[1] — score.py is one level deep (online-deployment/score.py), so parents[1] reaches the project root. Adjust if your layout differs.
• asyncio.get_event_loop().run_until_complete() bridges the sync run() to the async workflow.
• Optional Application Insights tracing configured via env var.
• Input key: Inspect the workflow to determine what key to parse from the request body (e.g. "text", "question"). Adapt accordingly.
• Factory import: init() imports the create_workflow factory from workflow.py. Each run() call invokes the factory to get a fresh workflow instance, avoiding RuntimeError: Workflow is already running on concurrent requests.

Step 2 — Generate conda.yml

Use the template at ./assets/conda.yml.

Important:

• Do NOT pin version constraints unless the user specifies them. Packages like agent-framework-azure-ai-search may not have published version ranges on PyPI, which causes image build failures.
• Only include packages the workflow actually uses. For OpenAI API key workflows, include agent-framework-openai but omit agent-framework-azure-ai-search and azure-monitor-opentelemetry unless needed.

Step 3 — Generate endpoint.yml

Use the template at ./assets/endpoint.yml.

Step 4 — Generate deployment.yml (Template)

Use the template at ./assets/deployment.yml.

Critical — path resolution: AML CLI resolves all relative paths in the deployment YAML relative to the YAML file's location, not the working directory. Since deployment files live in online-deployment/:

environment:
  conda_file: conda.yml               # ← same dir as deployment.yml
code_configuration:
  code: ..                             # ← parent dir = project root
  scoring_script: online-deployment/score.py  # ← relative to code root

Getting this wrong causes a double-nesting error like online-deployment/online-deployment/conda.yml.

Other key settings:

• request_timeout_ms: 60000 — LLM calls typically take 5–30 s; the AML default of 5 s causes timeouts.
• Use conda_file (not pip_requirements) — the latter is not valid for inline environment definitions.
• When rendering with envsubst, use a restricted variable list so $schema is not eaten.
• Only include env vars the workflow actually needs; omit unused ones.

Security: The rendered YAML (deployment-rendered.yml) may contain API keys in plaintext. A .gitignore file is generated automatically to exclude it (see Step 4b).

Step 4b — Generate .gitignore

Always create online-deployment/.gitignore to prevent rendered YAML files containing secrets from being committed:

deployment-rendered.yml

Step 5 — Deploy

Option A: Bash script (Linux/macOS)

Use the template at ./assets/deploy.sh. Requires envsubst (part of gettext).

Option B: Direct CLI commands (Windows / any OS)

On Windows, deploy.sh won't work (envsubst, mktemp, process substitution are unavailable). Instead, run the steps directly in PowerShell:

# 1. Render deployment YAML (replace placeholders with actual values)
$content = Get-Content online-deployment/deployment.yml -Raw
$content = $content -replace '\$\{AZURE_OPENAI_ENDPOINT\}', $env:AZURE_OPENAI_ENDPOINT
# ... repeat for each placeholder ...
Set-Content -Path online-deployment/deployment-rendered.yml -Value $content

# 2. Create endpoint
az ml online-endpoint create `
  --subscription $SUBSCRIPTION_ID `
  --resource-group $RESOURCE_GROUP `
  --workspace-name $WORKSPACE_NAME `
  --file online-deployment/endpoint.yml

# 3. Create deployment (run from the project root directory!)
az ml online-deployment create `
  --subscription $SUBSCRIPTION_ID `
  --resource-group $RESOURCE_GROUP `
  --workspace-name $WORKSPACE_NAME `
  --file online-deployment/deployment-rendered.yml `
  --all-traffic

# 4. Smoke test
Set-Content -Path online-deployment/request.json -Value '{"text": "Hello"}'
az ml online-endpoint invoke `
  --subscription $SUBSCRIPTION_ID `
  --resource-group $RESOURCE_GROUP `
  --workspace-name $WORKSPACE_NAME `
  --name <ENDPOINT_NAME> `
  --request-file online-deployment/request.json

Important: Run the az ml online-deployment create command from the project root directory, not from inside online-deployment/. The CLI resolves code: .. relative to the YAML file, but the CWD also matters for finding the YAML file itself.

Step 6 — RBAC for Managed Identity

After the endpoint is created, its system-assigned managed identity needs the Cognitive Services User role on the Foundry resource.

Finding the AI Services resource

• Standalone AML workspace: The user must provide the AI Services (Cognitive Services) resource name and resource group.
• Foundry hub-based project: The AI Services resource is linked to the hub. Find it via the Azure Portal (Hub → Connected resources) or via CLI:

  az ml workspace show \
    --name <project-name> \
    --resource-group <rg> \
    --query "associated_workspaces" -o table
  

Assign the role

# Get principal ID
PRINCIPAL_ID=$(az ml online-endpoint show \
  --subscription "$SUBSCRIPTION_ID" \
  --name <ENDPOINT_NAME> \
  --resource-group "$RESOURCE_GROUP" \
  --workspace-name "$WORKSPACE_NAME" \
  --query identity.principal_id -o tsv)

# Assign role
az role assignment create \
  --assignee-object-id "$PRINCIPAL_ID" \
  --assignee-principal-type ServicePrincipal \
  --role "Cognitive Services User" \
  --scope "/subscriptions/<sub>/resourceGroups/<rg>/providers/Microsoft.CognitiveServices/accounts/<account>"

Why Cognitive Services User?

• Azure AI Developer does not include Microsoft.CognitiveServices/accounts/AIServices/agents/write.
• Cognitive Services User has the wildcard Microsoft.CognitiveServices/*.
• Allow 5–10 minutes for RBAC data plane propagation.

Foundry hub-based project — additional considerations

• The hub's managed network may restrict outbound access. Ensure the endpoint can reach the AI Services resource and any external APIs the workflow calls. If the hub uses a private endpoint, no extra steps are needed for AI Services calls within the same VNet.
• The user deploying must have the Azure AI Developer role (or equivalent) on the resource group to create endpoints and deployments in the project.

See ./references/managed-identity.md for full details.

Troubleshooting

See ./references/troubleshooting.md for extended diagnostics.

Source: microsoft/promptflow — distributed by TomeVault.