Azure Functions shipped a serverless agents runtime in public preview at Build 2026. Agents are defined in .agent.md markdown ...

Overview:  Functional testing tools help teams verify that software works as expected across web, mobile, and API environments.Different tools serve differ ...

This sample demonstrates a .NET web App and native client calling a .NET web API that is secured using Azure AD. Further on the API will also call a downstream API on-behalf of the signed-in user. The ...

In terms of the agents you build, Bayer put up its own agent system on Foundry, and now it has 20,000 of its own employees on it.

Getting started is simple! To run this sample in Azure Government you will need: An Azure Active Directory (Azure AD) tenant in Azure Government. You must have an Azure Government subscription in ...

Quick Summary Looking to ensure the reliability and performance of your APIs across complex applications? Discover the top 7 API automation testing tools for software developers in 2026 that help ...

Come along with me on a journey as we delve into the swirling, echoing madness of identity attacks. Today, I present a case study on how different implementations of OAuth 2.0, the core authentication ...

Microsoft’s declarative REST and GraphQL API design tool supports MySQL and Postgres as well as Azure databases and works anywhere you can run a Docker container. Microsoft Azure is now so big it’s ...

Applications connected to Azure through its API Connections functionality could have leaked data to unauthenticated or low-privileged users prior to Microsoft fixing the issue earlier this year, ...

As AI ecosystems become increasingly complex, seamless interoperability and effective context-sharing among models, agents, and tools are essential. The Model Context Protocol (MCP) serves as a kind ...

GitHub Copilot for Azure just shipped with an important addition since its debut at Ignite 2024 as a private preview. The Visual Studio Code extension is no longer just an assistant -- it's now an ...

One of the biggest issues with large language models (LLMs) is working with your own data. They may have been trained on terabytes of text from across the internet, but that only provides them with a ...