Skill UI

This SDK allows developers to build persistent, sophisticated AI agents directly on Azure AI Foundry. It supports complex conversational workflows by integrating external functions (tools), enabling code execution, web search, and maintaining conversation history through dedicated thread management. Ideal for developing enterprise-grade, production-ready AI applications.

This guide details how to use the Azure AI Projects SDK to programmatically define and deploy sophisticated, container-based hosted agents. It covers essential steps including setting up prerequisites, managing container images, specifying resource limits (CPU/memory), and integrating various tools like code interpreters, enabling robust AI agent deployment in Azure AI Foundry.

This is a high-level SDK for .NET that provides comprehensive client access to Azure AI Foundry services. It enables developers to manage complex AI workflows, including creating and managing conversational agents, handling data connections, uploading datasets, managing model deployments, and setting up search indexes within an Azure AI project structure. Ideal for integrating advanced AI capabilities into enterprise applications.

This is a high-level Software Development Kit (SDK) designed for managing the full lifecycle of AI projects within Azure AI Foundry. Built for Java, it provides robust client interfaces for core AI operations, including managing connections to Azure resources, uploading and managing datasets, creating and updating search indexes, running model evaluations, and handling deployments. It streamlines complex AI workflow orchestration.

This is a high-level Software Development Kit (SDK) designed for managing the full lifecycle of AI projects within Azure AI Foundry. Built for Java, it provides robust client interfaces for core AI operations, including managing connections to Azure resources, uploading and managing datasets, creating and updating search indexes, running model evaluations, and handling deployments. It streamlines complex AI workflow orchestration.

The Azure AI Projects SDK enables developers to build and manage sophisticated, end-to-end AI applications directly on Microsoft Foundry. It provides comprehensive client operations for managing agents, datasets, model deployments, and indexes. Key features include integrating diverse tools (like code interpreters and file search) and handling complex conversational flows, making it ideal for building production-grade AI workflows.

The Azure AI Projects SDK enables developers to build and manage sophisticated, end-to-end AI applications directly on Microsoft Foundry. It provides comprehensive client operations for managing agents, datasets, model deployments, and indexes. Key features include integrating diverse tools (like code interpreters and file search) and handling complex conversational flows, making it ideal for building production-grade AI workflows.

This is a high-level SDK designed for managing the complete lifecycle of AI projects within Azure AI Foundry. It provides comprehensive tools in TypeScript to create, manage, and evaluate sophisticated AI agents, handle resource connections, process datasets, deploy models, and build custom search indexes.

This is a high-level SDK designed for managing the complete lifecycle of AI projects within Azure AI Foundry. It provides comprehensive tools in TypeScript to create, manage, and evaluate sophisticated AI agents, handle resource connections, process datasets, deploy models, and build custom search indexes.

This guide provides a comprehensive workflow for building and managing container-based hosted agents using the Azure AI Projects SDK in Python. It covers defining agents with specific container images, allocating computational resources (CPU/Memory), and integrating tools like code interpreters. This is essential for deploying robust, production-grade AI agents within Azure AI Foundry environments.

This guide details the process of building Model Context Protocol (MCP) servers. These servers are crucial for enabling Large Language Models (LLMs) to interact with external APIs and services, facilitating complex, real-world task completion. It provides comprehensive coverage on architectural planning, choosing appropriate languages (Python, TypeScript, C#), and selecting transport mechanisms (HTTP, stdio) for robust integration within enterprise ecosystems like Azure and Foundry.

This comprehensive guide outlines the best practices and structured methodology for developing specialized skills for AI coding agents. It provides detailed instructions on integrating Azure SDKs and Microsoft Foundry services, covering crucial topics like secure authentication patterns (DefaultAzureCredential), modular workflow design, and maintaining concise, effective knowledge context for optimal agent performance.