Sakthai Workflow AI Control Hub 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

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:

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

💡 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.

Agent Development Lifecycle

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

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:

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.

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:

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

Source: sakthai-workflow/ai-control-hub — distributed by TomeVault.