---

name: microsoft-foundry description: "Build, deploy, evaluate, optimize, fine-tune, and manage Microsoft Foundry agents, models, and resources end to end. USE FOR: foundry, azd ai agent, azd provision/deploy, hosted agent scaffold/develop/run/deploy/troubleshoot, prompt agent create, create agent, update agent, add tool to agent, invoke agent, agent.yaml, agent insights, pull agent insights, evaluate agent, batch eval, continuous eval, continuous monitoring, agent CI/CD, optimize prompt, improve prompt, prompt optimizer, optimize agent instructions, Agent Optimizer scaffold, dataset curation from traces, deploy model, model fine-tuning (SFT/DPO/RFT), Foundry project, RBAC, role assignment, permissions, quota, capacity, region, deployment failure, AI Services, create Foundry resource, knowledge index, customize deployment, onboard, availability, training-data, grader, distillation, 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: "1.2.26"

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

Follow each applicable subsection below before starting its corresponding action or workflow.

Dependency Check and Setup

MANDATORY: As the first step after this skill loads, run the dependency check and setup script below from this skill's root and wait for it to finish before continuing. The script checks first and installs only missing dependencies; it does not reinstall dependencies that are already available.

You MUST complete this check before reading or entering any sub-skill, workflow, or workflow-specific reference.

./scripts/check-and-setup-dependencies.sh     # macOS / Linux
./scripts/check-and-setup-dependencies.ps1    # Windows (pwsh)

Strictly follow the script output for subsequent actions.

Workflow Guidance

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.

Foundry MCP

MANDATORY: Before using Foundry MCP operations, call the Azure MCP foundry tool and inspect the available Foundry MCP tools and related parameters. Treat this as the discovery/help step for MCP-based workflows.

azd

MANDATORY: Before executing ANY azd command, you MUST read azd-guidance and strictly follow the shared rules defined in it, especially the AZURE_DEV_USER_AGENT setting rules.

Sub-Skills

This skill includes specialized sub-skills for specific workflows. When a sub-skill matches the task, strictly follow its workflow:

| Sub-Skill | When to Use | Reference | |-----------|-------------|-----------| | deploy | Deploy hosted agents to Foundry, smoke-test a deployment, create or update prompt agents, and manage agent versions and multi-environment deploys. | deploy | | cicd | Set up a CI/CD deployment pipeline for a Foundry agent. | cicd | | invoke | Send messages to an agent, single or multi-turn conversations | invoke | | routine | Schedule or event-trigger Foundry agents with routines; use azd for CRUD, enable/disable, manual dispatch, and viewing past runs, or define routines in azure.yaml. | routine | | 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 | | 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 | | insights | Pull generated agent insights, evidence, and recommendations from an existing monitor; read-only retrieval, not a new analysis run | insights | | 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 | | validate | Use only when the user explicitly asks to use this validation sub-skill or to validate Microsoft Foundry hosted-agent code against best practices. Never invoke it proactively or add it to another workflow. | validate | | create (quick start) | Create a new hosted Foundry agent from scratch end-to-end — scaffold, provision or use an existing Foundry project, deploy, and smoke-test. Do not use for any work on existing code. For anything not covered by the quickstart, use create. | create/quick-start-hosted.md | | create | Use when the standard end-to-end happy path (quick start) doesn't fit. Create a new Foundry agent, update code of an existing agent, continue development of an existing agent, wire connections at scaffold time, use advanced setup or A2A (Agent2Agent), or recover from a failed quickstart run. | create | | 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 | | 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 Microsoft 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 Microsoft 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 | | azd-guidance | Provide shared azd knowledge and guidance for managing Foundry agents. Read this first for any workflows related to azd. | azd-guidance |

💡 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 hosted agent end-to-end (scaffold + deploy + test) | dependency check and setup → azd-guidance → quick-start-hosted (self-contained end-to-end) | | Anything beyond the standard quickstart (existing code, migration, re-hosting, deployment customization, scaffold-time connections, A2A (Agent2Agent), recovery) | dependency check and setup → azd-guidance → create → deploy → invoke | | Optimize existing Python hosted agent | dependency check and setup → azd-guidance → agent-optimizer → scaffold/review → eval.yaml → optimize → apply candidate → deploy → invoke | | Deploy an agent (code already exists)