microsoft-foundry
End-to-end deployment, evaluation, and management of AI agents on Microsoft Foundry.
- Covers the complete agent lifecycle: creation from starter samples, containerization and ACR push, hosted or prompt agent deployment, invocation, batch evaluation, and prompt optimization
- Includes specialized sub-skills for deploy, invoke, observe (evaluation and prompt optimization), trace analysis, troubleshooting, and dataset curation from production traces
- Supports project and resource provisioning, RBAC management, quota tracking, and model deployment with intelligent routing across regions and SKUs
- Requires
.foundry/agent-metadata.yamlas the source of truth for environment-specific configuration, datasets, and evaluation test cases
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
foundrytool and inspect the available Foundry MCP tools and related parameters. Treat this initialfoundrycall as a discovery/help step. For this skill, Azure MCPfoundryis 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 |
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