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microsoft-foundry

Deploy, evaluate, and troubleshoot Foundry agents and models in Azure AI Foundry.

Updated Apr 19, 2026
Search Tags:#model deployment#agent management#foundry#azure ai#ai foundry
One-click install
npx skills add https://github.com/Avihai-H/infraops --skill microsoft-foundry-avihai-h
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: microsoft-foundry
Source: https://github.com/Avihai-H/infraops/tree/main/.github/skills/microsoft-foundry
Command: npx skills add https://github.com/Avihai-H/infraops --skill microsoft-foundry-avihai-h

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires azure-cli, azure-ai-projects, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill unit provides end-to-end management of Foundry agents and models, enabling efficient deployment, evaluation, and troubleshooting of AI agents within the Azure AI Foundry ecosystem.

Core Features & Use Cases

  • Agent Deployment and Management: Deploy, manage, and invoke Foundry agents, including Docker containerization, model deployment, and lifecycle management.
  • Model Deployment: Deploy Azure OpenAI models with intelligent routing, handling quick preset deployments, fully customized deployments, and capacity discovery.
  • Evaluation and Optimization: Evaluate agent performance, optimize prompts, and manage datasets for agent training.
  • Resource Management: Manage quotas, capacity, and RBAC permissions for Foundry resources.
  • Use Case: Use this Skill to deploy a new agent to the Foundry, configure and deploy a model, or troubleshoot issues with existing deployments.

Quick Start

Deploy a new Foundry agent and model using the 'microsoft-foundry' skill.

Frequently Asked Questions about microsoft-foundry

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

FAQPage Schema
How do I deploy Azure OpenAI models with intelligent routing in Azure AI Foundry?▼

To deploy Azure OpenAI models, you can use this Skill to handle quick preset deployments, fully customized configurations, and capacity discovery directly within the Azure AI Foundry ecosystem. It streamlines the entire model deployment workflow using the Azure CLI and Python SDK.

What is the best way to manage Foundry agent lifecycles and Docker containerization?▼

Managing Foundry agents involves deploying, invoking, and troubleshooting agents, including Docker containerization and model deployment. This Skill provides end-to-end agent management, handling everything from initial deployment to lifecycle optimization within Azure AI Foundry.

Can I evaluate agent performance and optimize prompts in Azure AI Foundry?▼

Yes, you can evaluate agent performance, optimize prompts, and manage datasets for agent training. The Skill focuses on evaluation and optimization workflows to help you troubleshoot and improve existing Foundry deployments.

Do I need Azure CLI and Python SDK access to manage Foundry agents?▼

Yes, you need the Azure CLI, the azure-ai-projects Python SDK, and access to the Azure AI Foundry platform to use this Skill. These dependencies are required to execute deployment workflows, manage resources, and configure RBAC permissions.

How do I manage quotas, capacity, and RBAC permissions for Foundry resources?▼

You can manage quotas, capacity, and RBAC permissions for Foundry resources using the Skill's resource management capabilities. It integrates with the Azure CLI and Python SDK to configure and monitor your Azure AI Foundry platform allocations.

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