Build & Tooling

Amazon SageMaker Unified Studio is a cutting-edge all-in-one development environment that integrates data analytics and machine learning workflows, addressing the need to unify disparate tasks in modern enterprises. This article explores the technical architecture, core capabilities, and governance features of SageMaker Unified Studio, offering valuable insights for AI developers and builders.

This code snippet demonstrates how to use agent operations with the Model Context Protocol (MCP) tool from the Azure Agents service using a synchronous client. It provides a practical example of creating an agent with MCP tool and processing agent runs within an Azure AI project environment.

This code snippet demonstrates how to use agents with Logic Apps to execute the task of sending an email. It provides a sample implementation using Azure AI agents and Logic Apps, along with instructions on prerequisites and usage.

This code snippet demonstrates how to use agent operations with file searching from the Azure Agents service using a synchronous client. It showcases uploading a file, creating a vector store, and creating an agent with a file search tool, providing hands-on experience with Azure AI Projects and Agents SDK.

This code snippet demonstrates how to add files to an agent during the vector store creation using Azure AI tools. It involves setting up the necessary environment variables, creating a vector store, and utilizing Azure services for data storage and retrieval.

This code snippet demonstrates how to use Azure Agents service with the Deep Research tool through a Python client. It includes functions for converting citation markers in markdown content to HTML superscript format. The sample provides practical guidance on integrating Azure AI services and processing text data.

This code snippet demonstrates how to use the Azure Agents service with the Browser Automation tool for browser automation tasks. It shows how to create a new agent with the Browser Automation tool attached, enabling the agent to perform tasks related to web browsing.

This code snippet demonstrates how to use Azure Function agent operations from the Azure Agents service using a synchronous client. It showcases setting up an Azure Function tool to interact with a bot and provides guidance on required environment variables and dependencies. The learning value includes understanding how to integrate Azure Functions with AI agents for automated tasks.

This code snippet demonstrates how to enable automatic function calls in Azure AI agents using Python. It shows how to create an agent, set up functions to be called automatically, and initialize the agent for use in AI projects. The code provides a practical example for integrating AI agents with custom functions.

This article provides a step-by-step guide for .NET developers to create a modular multi-agent system using Google's A2A, Microsoft's Semantic Kernel, and Semantic Kernel C# SDK. Readers will learn about the architecture of multi-agent systems, setting up A2A servers and clients, and real-time orchestration, offering practical insights into building AI agents that collaborate seamlessly.

This article introduces the Model Context Protocol (MCP) and its significance for Azure AI Foundry, enabling seamless integration between AI agents and tools without manual coding. By leveraging MCP, developers can enhance agent development speed, reduce maintenance overhead, and achieve interoperability across Azure and OpenAI platforms.

Azure AI Foundry is a comprehensive web portal that consolidates various Azure AI services, facilitating collaborative AI project development, model deployment, testing, and integration of custom data sources. AI developers can leverage Azure AI Foundry to create generative AI applications, deploy models to real-time endpoints, define workflows, and incorporate multiple AI capabilities through Azure AI Services.

The article introduces AWS SageMaker Unified Studio, an all-in-one interface that streamlines the machine learning and AI development lifecycle. It highlights the accessibility of data, collaboration features, model training capabilities, and integration with Amazon Q Developer for code assistance, making it suitable for various users from research projects to enterprise teams.

This repository provides a pre-built LangGraph tools agent for the Open Agent Platform, enabling users to work with MCP servers and a LangConnect RAG tool. It includes setup instructions and integration details with the Open Agent Platform, making it a valuable resource for developers interested in agent orchestration and tooling automation.

The AI for Beginners curriculum by Microsoft offers a comprehensive 12-week program with 24 lessons covering practical AI concepts, quizzes, and labs. This resource is ideal for AI developers and builders looking to start their journey in artificial intelligence.