microsoft-foundry

name	microsoft-foundry
description	Deploy, evaluate, fine-tune, and manage Foundry agents end-to-end: Docker build, ACR push, hosted/prompt agent create, container start, batch eval, continuous eval, prompt optimizer, agent.yaml, dataset curation from traces, model fine-tuning (SFT/DPO/RFT). USE FOR: deploy agent, hosted agent, create agent, invoke agent, evaluate agent, continuous eval, continuous monitoring, optimize prompt, improve prompt, prompt optimizer, optimize agent instructions, improve agent instructions, optimize system prompt, deploy model, Foundry project, RBAC, role assignment, permissions, quota, capacity, region, troubleshoot agent, deployment failure, AI Services, create Foundry resource, provision, knowledge index, agent monitoring, customize deployment, onboard, availability, fine-tune, SFT, DPO, RFT, training-data, grader, distillation, fine-tuned model, training job, large file upload. DO NOT USE FOR: Azure Functions, App Service, general Azure deploy (use azure-deploy), general Azure prep (use azure-prepare).
license	MIT
metadata	{"author":"Microsoft","version":"0.0.0-placeholder"}

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.

Pre-Execution Requirements

MANDATORY: Before executing ANY workflow, you MUST first call the Azure MCP foundry tool and inspect the available Foundry MCP tools and related parameters. Treat this initial foundry call as a discovery/help step. For this skill, Azure MCP foundry is the required entry point for Foundry-related MCP operations.

Sub-Skills

MANDATORY: Before executing ANY workflow-specific steps, you MUST read the corresponding sub-skill document. Do not call workflow-specific 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/clone agent deployments	deploy
invoke	Send messages to an agent, single or multi-turn conversations	invoke
observe	Evaluate agent quality, run batch evals, analyze failures, optimize prompts, improve agent instructions, compare versions, set up CI/CD monitoring, and enable continuous production evaluation	observe
trace	Query traces, analyze latency/failures, correlate eval results to specific responses via App Insights customEvents	trace
troubleshoot	View hosted agent logs, query telemetry, diagnose failures	troubleshoot
create	Create new hosted agent applications. Supports Microsoft Agent Framework, LangGraph, or custom frameworks in Python or C#, across responses or invocations protocols.	create
faos-optimize	Convert existing Python agent code to a FAOS (Foundry Agent Optimization Service) optimization-ready version by wiring evaluator-targeted instructions/model/temperature knobs, then stop for review before deployment.	faos-optimize
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
private-network	Answer questions about Foundry network isolation and deploy Foundry with VNet isolation (BYO VNet, Managed VNet, hybrid). Covers architecture concepts, template selection, deployment, and post-deployment validation.	resource/private-network/private-network.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
finetuning	Fine-tune models on Azure AI Foundry — SFT distillation, DPO preference optimization, RFT with graders and tool calling. Dataset preparation, grader calibration, training, checkpoint selection, deployment, evaluation. Use for: fine-tune, SFT, DPO, RFT, training data, grader, distillation, fine-tuned model, large file upload.	finetuning/SKILL.md

💡 Tip: For a complete onboarding flow: project/create (public) or private-network (VNet isolation) → models/deploy-model → agent workflows (create → deploy → invoke).

💡 Fine-Tuning: Use finetuning for all model customization — SFT distillation, DPO preference optimization, and RFT with graders. Includes quickstart, grader calibration, and training curve analysis.

💡 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.

💡 Prompt Optimization: For requests like "optimize my prompt" or "improve my agent instructions," load observe and use the prompt_optimize MCP tool through that eval-driven workflow.

Infrastructure Lifecycle

Match user intent to the correct infrastructure workflow.

User Intent	Workflow
"Create Foundry" / "Set up Foundry" (ambiguous)	Use AskUserQuestion: (a) just an AI Services resource, (b) a project with public access, or (c) a project with network isolation? Route: (a) → resource/create, (b) → project/create, (c) → private-network
Set up Foundry with VNet isolation	private-network
Create a Foundry project (public)	project/create
Create a bare Foundry resource	resource/create

Agent Development Lifecycle

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

User Intent	Workflow (read in order)
Create a new agent from scratch	create → deploy → invoke
Make existing Python agent FAOS optimizable	faos-optimize → review → deploy → invoke
Deploy an agent (code already exists)	deploy (includes eval-suite setup) → invoke → observe (evaluate/optimize)
Update/redeploy an agent after code changes	deploy (includes eval-suite setup) → invoke → observe (evaluate/optimize)
Invoke/test/chat with an agent	invoke
Optimize / improve agent prompt or instructions	observe (Step 4: Optimize)
Evaluate and optimize agent (full loop)	observe
Enable continuous evaluation monitoring	observe (Step 6: CI/CD & Monitoring)
Troubleshoot an agent issue	invoke → troubleshoot
Fix a broken agent (troubleshoot + redeploy)	invoke → troubleshoot → apply fixes → deploy → invoke

Agent: .foundry Workspace Standard

Every agent source folder should keep Foundry-specific state under .foundry/:

<agent-root>/
  .foundry/
    agent-metadata.yaml
    agent-metadata.prod.yaml
    datasets/
    evaluators/
    results/

• agent-metadata.yaml is the preferred local/dev metadata file. Optional sidecar files such as agent-metadata.prod.yaml can hold a single prod or CI-targeted environment without mixing multiple environments in one file.
• datasets/ and evaluators/ are local cache folders. Reuse them when they are current, and ask before refreshing or overwriting them.
• See Agent Metadata Contract for the canonical schema and workflow rules.

Agent: Setup References

• Standard Agent Setup - Standard capability-host setup with customer-managed data, search, and AI Services resources.

Agent: Project Context Resolution

Agent skills should run this step only when they need configuration values they don't already have. If a value (for example, agent root, environment, project endpoint, or 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: Discover Agent Roots

Search the workspace for .foundry/ folders that contain agent-metadata.yaml or agent-metadata.<env>.yaml.

• One match → use that agent root.
• Multiple matches → require the user to choose the target agent folder.
• No matches → for create/deploy workflows, seed a new .foundry/ folder during setup; for all other workflows, stop and ask the user which agent source folder to initialize.

After selecting an agent root, keep all local .foundry cache inspection, source inspection, evaluator suggestions, dataset suggestions, and prompt-optimization context inside that folder only. Do not scan sibling agent folders unless the user explicitly switches roots.

Step 2: Select Metadata File and Resolve Environment

Inside the selected agent root, choose the metadata file in this order:

1. Metadata filename or path explicitly provided by the user or workflow
2. If an explicit environment is already known and .foundry/agent-metadata.<env>.yaml exists, use that file
3. .foundry/agent-metadata.yaml
4. If multiple metadata files remain and no rule above selects one, prompt the user to choose

Read the selected metadata file and resolve the environment in this order:

1. Environment explicitly named by the user
2. If the selected metadata file defines exactly one environment, use it
3. Environment already selected earlier in the session
4. defaultEnvironment from metadata

If the selected metadata file still contains multiple environments and none of the rules above selects one, prompt the user to choose. Keep the selected agent root, metadata file, and environment visible in every workflow summary.

If the selected environment exposes older testSuites[] metadata but not evaluationSuites[], treat testSuites[] as the source for this session and normalize each entry in memory to the evaluationSuites[] shape before continuing. If the metadata is older still and only exposes legacy testCases[], normalize that list the same way. Preserve dataset and evaluator fields, keep any existing tags, and map legacy priority to tags.tier only when tags.tier is missing: P0 -> smoke, P1 -> regression, P2 -> coverage.

Step 3: Resolve Common Configuration

Use the selected environment in the selected metadata file as the primary source:

Metadata Field	Resolves To	Used By
environments.<env>.projectEndpoint	Project endpoint	deploy, invoke, observe, trace, troubleshoot
environments.<env>.agentName	Agent name	invoke, observe, trace, troubleshoot
environments.<env>.azureContainerRegistry	ACR registry name / image URL prefix	deploy
environments.<env>.evaluationSuites[]	Dataset + evaluator + tag bundles	observe, eval-datasets

Step 4: Bootstrap Missing Metadata (Create/Deploy Only)

If create/deploy is initializing a new .foundry workspace and metadata fields are still missing, check if azure.yaml exists in the project root. If found, run azd env get-values and use it to seed agent-metadata.yaml by default, or agent-metadata.<env>.yaml when the workflow explicitly targets a separate environment-specific file.

On any metadata write (deploy, auto-setup, dataset refresh, or trace-to-dataset update), persist only evaluationSuites[] in the selected metadata file. If the selected file is a preferred single-environment file, rewrite only that one environment block. If the selected file is a legacy multi-environment file, rewrite only the selected environment block. Never copy or merge environments across sibling metadata files automatically. If the selected environment still uses older testSuites[] or legacy testCases[], rewrite it to evaluationSuites[] and remove migrated priority fields from the rewritten entries.

azd Variable	Seeds
AZURE_AI_PROJECT_ENDPOINT or AZURE_AIPROJECT_ENDPOINT	environments.<env>.projectEndpoint
AZURE_CONTAINER_REGISTRY_NAME or AZURE_CONTAINER_REGISTRY_ENDPOINT	environments.<env>.azureContainerRegistry
AZURE_SUBSCRIPTION_ID	Azure subscription for trace/troubleshoot lookups

Step 5: Collect Missing Values

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

• Agent root — Target folder containing .foundry/agent-metadata*.yaml
• Metadata file — agent-metadata.yaml for local/dev, or an explicit sidecar such as agent-metadata.prod.yaml
• Environment — dev, prod, or another environment key from metadata
• Project endpoint — AI Foundry project endpoint URL
• Agent name — Name of the target agent

💡 Tip: If the user already provides the agent path, environment, project endpoint, or agent name, 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

SDK Quick Reference

• Python