microsoft-foundry

Manage Azure AI Foundry agent and model lifecycles, including deployment and evaluation.

Updated Apr 9, 2026

Search Tags:#ai #azure #agent #model #foundry

Ready to run

npx skills add https://github.com/paidethon/Daily --skill microsoft-foundry-paidethon

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: microsoft-foundry
Source: https://github.com/paidethon/Daily/tree/main/.agents/skills/microsoft-foundry
Command: npx skills add https://github.com/paidethon/Daily --skill microsoft-foundry-paidethon

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill simplifies the management of Foundry agents and models, including deployment, evaluation, and troubleshooting, making it easier to develop and maintain AI solutions.

Core Features & Use Cases

Agent Deployment: Deploy and manage Foundry agents with ease, including building, pushing to container registries, and creating hosted agents.
Model Evaluation: Evaluate agent quality, run batch evaluations, and analyze performance metrics.
Troubleshooting: Diagnose and resolve issues with agents and model deployments.
Use Case: Imagine you have a Foundry agent that needs to be deployed and evaluated. Use this Skill to quickly deploy the agent, evaluate its performance, and identify any issues that need to be addressed.

Quick Start

Use the microsoft-foundry skill to deploy the 'my-agent' agent to the 'my-project' project.

Frequently Asked Questions about microsoft-foundry

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

FAQPage Schema

How do I deploy an Azure AI Foundry agent using Docker and container registries?▼

To deploy an Azure AI Foundry agent, you need to build the Docker image, push it to Azure Container Registry, and then create a hosted agent within your Foundry project. This automates container management and agent lifecycle.

How does batch evaluation work for Azure AI Foundry models?▼

Batch evaluation for Azure AI Foundry models runs performance assessments across datasets to analyze metrics. It helps evaluate agent quality and optimize prompts by testing multiple interactions and aggregating the results.

What is the best way to troubleshoot Azure AI Foundry agent deployment issues?▼

Troubleshooting Azure AI Foundry agents involves diagnosing deployment failures by checking RBAC role assignments, permissions, quota, capacity, and region availability within your Foundry project to resolve configuration issues.

Do I need specific RBAC permissions to manage Foundry agents and models?▼

Yes, managing Foundry agents requires proper RBAC role assignments and permissions within your Azure AI Foundry project. You must configure capacity and quota settings to ensure successful agent deployment and model operations.

Can I optimize prompts and curate datasets for Foundry agents?▼

Yes, you can optimize prompts and curate datasets for Foundry agents. The skill supports prompt optimization and dataset curation to improve agent quality and ensure accurate batch evaluation results.