Microsoft Foundry

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

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.

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 | Deploy hosted agents to Foundry, smoke-test a deployment, create or update prompt agents, and manage agent versions and multi-environment deploys. | [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 (quick start) | Create a new hosted Foundry agent from scratch end-to-end — scaffold, provision a new Foundry project, deploy, and smoke-test. Opinionated happy-path that accepts common overrides (language, region, sample, topic, existing project, existing model). For anything not covered by the quickstart, use create. | [create/quick-start-hosted.md](foundry-agent/create/quick-start-hosted.md) | | create | Use when the standard end-to-end happy path doesn't fit — lifting existing agent code into the project, deploying outside the default code path, wiring connections at scaffold time, advanced setup, or recovering from a failed quickstart run. | [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 hosted agent end-to-end (scaffold + deploy + test) | [quick-start-hosted](foundry-agent/create/quick-start-hosted.md) (self-contained end-to-end) | | Anything beyond the standard quickstart (existing code, deployment customization, scaffold-time connections, recovery) | [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) → 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 can keep Foundry-specific cache and overlay state under .foundry/:

/
  .foundry/
    agent-metadata.yaml
    agent-metadata.prod.yaml
    suites/
    datasets/
    evaluators/
    results/

• In azd projects, derive deployment context (project endpoint, agent name/version, ACR, App Insights) from azure.yaml plus azd env get-values; do not duplicate those values in metadata when azd already provides them.
• agent-metadata.yaml is the preferred local/dev overlay for non-azd values, remote Foundry suite references, local cache paths, result summaries, and explicit overrides. Optional sidecar files such as agent-metadata.prod.yaml can hold a single prod or CI-targeted overlay without mixing multiple environments in one file.
• suites/, datasets/, and evaluators/ are local cache folders. Reuse them when they are current, and ask before refreshing or overwriting them.
• See [Agent Metadata Contract](references/agent-metadata-contract.md) for the canonical schema and workflow rules.

Agent: Setup References

• [Standard Agent Setup](references/standard-agent-setup.md) — advanced setup for production workloads that need data-residency control (bring-your-own Cosmos DB / Storage / AI Search via a Foundry capability host). The default azd ai agent flow uses Basic Agent Setup and does not provision capabilityHosts/agents — do not flag its absence as a bug. For default post-provision state, see the "Expected env-var fingerprint" section in [foundry-agent/create/create-hosted.md](foundry-agent/create/create-hosted.md).

Agent: Common 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 and azd Context

First check whether the workspace has azure.yaml with services using host: azure.ai.agent.

• One azd agent service -> use that service's project folder as the agent root.
• Multiple azd agent services -> require the user to choose the target service/folder.
• No azd agent service -> search the workspace for .foundry/ folders that contain agent-metadata.yaml or agent-metadata..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: Resolve Environment and Deployment Context

If azure.yaml is present, resolve the azd environment first:

1. Environment explicitly named by the user
2. AZURE_ENV_NAME from azd env get-values
3. azd default environment from .azure/config.json
4. Environment already selected earlier in the session

Run azd env get-values for the selected environment when project/deployment values are not already known. Prefer azd values for deployment context:

| azd Variable | Resolves To | |-------------|-------------| | AZURE_AI_PROJECT_ENDPOINT or AZURE_AIPROJECT_ENDPOINT | Project endpoint | | AGENT__NAME | Agent name for the selected azd service | | AGENT__VERSION | Agent version for the selected azd service | | AZURE_CONTAINER_REGISTRY_NAME or AZURE_CONTAINER_REGISTRY_ENDPOINT | ACR registry name / image URL prefix | | APPLICATIONINSIGHTS_CONNECTION_STRING | App Insights connection string for trace workflows | | AZURE_SUBSCRIPTION_ID, AZURE_RESOURCE_GROUP, AZURE_AI_ACCOUNT_NAME, AZURE_AI_PROJECT_NAME | Azure resource lookup and Playground links |

When azd supplies these values, use them as the source of truth and do not copy them into .foundry/agent-metadata*.yaml on metadata writes.

Step 3: Select Metadata Overlay 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..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 any remaining environment choice 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, environment, and whether context came from azd or metadata 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 4: Resolve eval.yaml Local Evaluation Intent

If eval.yaml exists in the selected agent root, parse it before generating new suites:

• agent.name -> target agent candidate; verify it matches the selected azd/metadata agent before using it.
• dataset.local_uri -> local seed dataset candidate; legacy dataset_file may be normalized in memory.
• dataset.name / dataset.version -> registered dataset candidate.
• validation_dataset -> optional validation dataset candidate.
• evaluators[] -> candidate Foundry evaluator names; verify with evaluator_catalog_get before treating them as remote evaluators.
• name -> local eval/suite candidate; verify remotely before persisting as suiteName.
• options.eval_model, options.optimization_model, options.max_candidates, options.optimization_config.model_search_space, options.pass_threshold, max_samples, trace_days, and generation_instruction -> setup defaults.

Treat eval.yaml as local evaluation intent, not proof that a Foundry suite exists. Persist synced suite/dataset/evaluator references to .foundry

…

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: microsoft
• Source: microsoft/azure-skills
• License: MIT

Install and usage instructions live in the source repository linked above.