microsoft-foundry

What this skill does

# 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](foundry-agent/deploy/deploy.md) |
| **invoke** | Send messages to an agent, single or multi-turn conversations | [invoke](foundry-agent/invoke/invoke.md) |
| **invocations-ws** | Build, deploy, and connect to hosted agents that speak the `invocations_ws` duplex WebSocket protocol — voice agents, real-time streams, and signaling for out-of-band media transports. | [invocations-ws](foundry-agent/invocations-ws/invocations-ws.md) |
| **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](foundry-agent/observe/observe.md) |
| **trace** | Query traces, analyze latency/failures, correlate eval results to specific responses via App Insights `customEvents` | [trace](foundry-agent/trace/trace.md) |
| **troubleshoot** | View hosted agent logs, query telemetry, diagnose failures | [troubleshoot](foundry-agent/troubleshoot/troubleshoot.md) |
| **create** | Create new hosted agent applications. Supports Microsoft Agent Framework, LangGraph, or custom frameworks in Python or C#, across `responses`, `invocations`, or `invocations_ws` protocols. | [create](foundry-agent/create/create-hosted.md) |
| **agent-optimizer** | Make existing Python hosted-agent code optimization-ready, configure eval.yaml, run Agent Optimizer jobs, apply candidates locally, and deploy through azd after review. | [agent-optimizer](foundry-agent/agent-optimizer/agent-optimizer.md) |
| **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](foundry-agent/eval-datasets/eval-datasets.md) |
| **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](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](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](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](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](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](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](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](foundry-agent/observe/observe.md) 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](resource/create/create-foundry-resource.md), (b) → [project/create](project/create/create-foundry-project.md), (c) → [private-network](resource/private-network/private-network.md) |
| Set up Foundry with VNet isolation | [private-network](resource/private-network/private-network.md) |
| Create a Foundry project (public) | [project/create](project/create/create-foundry-project.md) |
| Create a bare Foundry resource | [resource/create](resource/create/create-foundry-resource.md) |

## 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](foundry-agent/create/create-hosted.md) → [deploy](foundry-agent/deploy/deploy.md) → [invoke](foundry-agent/invoke/invoke.md) |
| Optimize existing Python hosted agent | [agent-optimizer](foundry-agent/agent-optimizer/agent-optimizer.md) → scaffold/review → eval.yaml → optimize → apply candidate → 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) → invok