Microsoft Foundry Skill

This skill helps developers work with Microsoft Foundry resources, covering model discovery and deployment, complete dev lifecycle of AI agent, evaluation workflows, and troubleshooting.

Sub-Skills

MANDATORY: Before executing ANY workflow, you MUST read the corresponding sub-skill document. Do not call MCP tools for a workflow without reading its skill document. This applies even if you already know the MCP tool parameters — the skill document contains required workflow steps, pre-checks, and validation logic that must be followed. This rule applies on every new user message that triggers a different workflow, even if the skill is already loaded.

This skill includes specialized sub-skills for specific workflows. Use these instead of the main skill when they match your task:

Sub-Skill	When to Use	Reference
deploy	Containerize, build, push to ACR, create/update/start/stop/clone agent deployments	deploy
invoke	Send messages to an agent, single or multi-turn conversations	invoke
observe	Eval-driven optimization loop: evaluate → analyze → optimize → compare → iterate	observe
trace	Query traces, analyze latency/failures, correlate eval results to specific responses via App Insights customEvents	trace
troubleshoot	View container logs, query telemetry, diagnose failures	troubleshoot
create	Create new hosted agent applications. Supports Microsoft Agent Framework, LangGraph, or custom frameworks in Python or C#. Downloads starter samples from foundry-samples repo.	create
eval-datasets	Harvest production traces into evaluation datasets, manage dataset versions and splits, track evaluation metrics over time, detect regressions, and maintain full lineage from trace to deployment. Use for: create dataset from traces, dataset versioning, evaluation trending, regression detection, dataset comparison, eval lineage.	eval-datasets
project/create	Creating a new Azure AI Foundry project for hosting agents and models. Use when onboarding to Foundry or setting up new infrastructure.	project/create/create-foundry-project.md
resource/create	Creating Azure AI Services multi-service resource (Foundry resource) using Azure CLI. Use when manually provisioning AI Services resources with granular control.	resource/create/create-foundry-resource.md
models/deploy-model	Unified model deployment with intelligent routing. Handles quick preset deployments, fully customized deployments (version/SKU/capacity/RAI), and capacity discovery across regions. Routes to sub-skills: preset (quick deploy), customize (full control), capacity (find availability).	models/deploy-model/SKILL.md
quota	Managing quotas and capacity for Microsoft Foundry resources. Use when checking quota usage, troubleshooting deployment failures due to insufficient quota, requesting quota increases, or planning capacity.	quota/quota.md
rbac	Managing RBAC permissions, role assignments, managed identities, and service principals for Microsoft Foundry resources. Use for access control, auditing permissions, and CI/CD setup.	rbac/rbac.md

💡 Tip: For a complete onboarding flow: project/create → agent workflows (deploy → invoke).

💡 Model Deployment: Use models/deploy-model for all deployment scenarios — it intelligently routes between quick preset deployment, customized deployment with full control, and capacity discovery across regions.

Agent Development Lifecycle

Match user intent to the correct workflow. Read each sub-skill in order before executing.

User Intent	Workflow (read in order)
Create a new agent from scratch	create → deploy → invoke
Deploy an agent (code already exists)	deploy → invoke
Update/redeploy an agent after code changes	deploy → invoke
Invoke/test/chat with an agent	invoke
Troubleshoot an agent issue	invoke → troubleshoot
Fix a broken agent (troubleshoot + redeploy)	invoke → troubleshoot → apply fixes → deploy → invoke
Start/stop agent container	deploy

Agent: Project Context Resolution

Agent skills should run this step only when they need configuration values they don't already have. If a value (e.g., project endpoint, agent name) is already known from the user's message or a previous skill in the same session, skip resolution for that value.

Step 1: Detect azd Project

If any required configuration value is missing, check if azure.yaml exists in the project root (workspace root or user-specified project path). If found, run azd env get-values to load environment variables.

Step 2: Resolve Common Configuration

Match missing values against the azd environment:

azd Variable	Resolves To	Used By
AZURE_AI_PROJECT_ENDPOINT or AZURE_AIPROJECT_ENDPOINT	Project endpoint	deploy, invoke, troubleshoot
AZURE_CONTAINER_REGISTRY_NAME or AZURE_CONTAINER_REGISTRY_ENDPOINT	ACR registry name / image URL prefix	deploy
AZURE_SUBSCRIPTION_ID	Azure subscription	troubleshoot

Step 3: Collect Missing Values

Use the ask_user or askQuestions tool only for values not resolved from the user's message, session context, or azd environment. Common values skills may need:

• Project endpoint — AI Foundry project endpoint URL
• Agent name — Name of the target agent

💡 Tip: If the user provides a project endpoint or agent name in their initial message, extract it directly — do not ask again.

Agent: Agent Types

All agent skills support two agent types:

Type	Kind	Description
Prompt	"prompt"	LLM-based agents backed by a model deployment
Hosted	"hosted"	Container-based agents running custom code

Use agent_get MCP tool to determine an agent's type when needed.

Tool Usage Conventions

• Use the ask_user or askQuestions tool whenever collecting information from the user
• Use the task or runSubagent tool to delegate long-running or independent sub-tasks (e.g., env var scanning, status polling, Dockerfile generation)
• Prefer Azure MCP tools over direct CLI commands when available
• Reference official Microsoft documentation URLs instead of embedding CLI command syntax

Additional Resources

• Foundry Hosted Agents
• Foundry Agent Runtime Components
• Foundry Samples

SDK Quick Reference

• Python