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microsoft-foundry

Coordinate Azure AI Foundry deployments and resource management across regions.

Updated Apr 7, 2026
Search Tags:#automation#azure#rbac#deploy#foundry#capacity#hosted-agents
One-click install
npx skills add https://github.com/bzellman/earp-kit --skill microsoft-foundry-bzellman
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: microsoft-foundry
Source: https://github.com/bzellman/earp-kit/tree/main/skills/system/microsoft-foundry
Command: npx skills add https://github.com/bzellman/earp-kit --skill microsoft-foundry-bzellman

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This skill helps developers and operators coordinate the deployment, evaluation, RBAC, and observability workflows for Azure AI Foundry resources and hosted agents across projects and regions.

Core Features & Use Cases

  • End-to-end Foundry lifecycle orchestration: provisioning resources, creating projects, deploying agents, and managing models.
  • Observability-first workflows: connect App Insights, run evals, and monitor deployments for quality and reliability.
  • RBAC & governance: manage access, roles, and permissions to Foundry resources across environments.

Quick Start

Set up a new Foundry project, deploy an agent, and enable observability end-to-end.

Frequently Asked Questions about microsoft-foundry

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I orchestrate Azure AI Foundry deployments across multiple regions?▼

Azure AI Foundry deployments are orchestrated by coordinating multi-phase workflows using Azure CLI and Foundry SDK surfaces to provision resources, manage capacity, and deploy agents across environments with guardrails and error handling.

What is the best way to manage RBAC and access roles for Azure AI Foundry projects?▼

RBAC for Foundry projects is managed through governance workflows that coordinate access, roles, and permissions across environments. This ensures secure resource management and proper governance for deployed agents and models.

Can I connect Application Insights to monitor Azure AI Foundry agent deployments?▼

Yes, you can connect Application Insights to enable observability-first workflows. This allows you to run evaluations, monitor agent deployments for quality and reliability, and troubleshoot issues across your Foundry resources.

How do I set up a new Foundry project and deploy a hosted agent end-to-end?▼

Setting up a new Foundry project involves provisioning resources, creating the project, deploying the hosted agent, and enabling observability. This end-to-end lifecycle orchestration uses Azure CLI and Foundry SDK surfaces with guardrails.

Does this Foundry deployment approach support capacity planning and troubleshooting?▼

Yes, capacity planning and troubleshooting are supported as core multi-phase workflows. The skill uses MCP tools and Foundry SDK surfaces to observe deployments, troubleshoot agents, and manage capacity across regions and environments.

Do I need Azure CLI and Foundry SDK surfaces to automate Foundry resource management?▼

Azure CLI, MCP tools, and Foundry SDK surfaces are used to automate provisioning, deploying, observing, and troubleshooting Foundry agents and resources. These tools enable multi-phase workflows with guardrails and error handling.

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