Showing category results for MCP

Available in Public Preview Today   Toolbox is a new way to curate, configure, and reuse tools across all of your AI agents without rewiring them every time from Foundry.  Today, teams build agents across different frameworks and runtimes. Each agent often wires tools directly, with its own authentication, credentials, and integration code. As ...

When we launched Microsoft Agent Framework last October, we made a promise: building production-grade AI agents should feel as natural and structured as building any other software. Today, we’re delivering on that promise — with the v1.0 release of Microsoft Agent Framework and the general availability of Foundry Toolkit for Visual Studio Code (...

Agents are already transforming how developers solve problems. Whether it's a coding agent that refactors your repo overnight, a research agent that synthesizes hundreds of documents into a brief, or an ops agent that monitors and remediates infrastructure — the pattern is clear. Developers are building agents that don't just answer questions, they...

We welcome guest author, Matt Kruczek, to share how to build a complete Skills Executor in .NET, combining Azure OpenAI with the Model Context Protocol (MCP) C# SDK to bring the same agentic patterns to the Microsoft ecosystem. You’ll see how to define reusable skills, connect local and external tools, and orchestrate intelligent agent loops—so ent...

MCP (Model Context Protocol) is a standard protocol that enables AI agents to securely connect with apps, data, and systems, supporting easy interoperability and seamless platform expansion. At Ignite, Microsoft Foundry introduced Foundry Tools, which serves as a central hub for discovering, connecting, and managing both public and private MCP tool...

Give your agents the power to remember  Imagine your agent never asks the same question twice. Until now, most agents have been stateless. Each conversation resets to zero, forgetting what users said just minutes ago or weeks ago. Developers tried to bridge this gap with homegrown solutions — storing embeddings in databases, manually retrieving pr...

In today’s rapidly evolving AI landscape, developers are seeking reliable, secure, and predictable language capabilities to power the next generation of enterprise-grade agents. As agentic architecture becomes central to modern applications, teams need tools that deliver stronger privacy guarantees, deterministic behavior, and seamless integration ...

Why agents need a new foundation Over the last year, developers have been experimenting with AI agents in every imaginable form. Agents are not just chatbots or copilots — they are autonomous software components that can reason about goals, call tools and APIs, collaborate with other agents, and adapt dynamically. Whether it’s a retrieval agent fo...

Generative-AI agents only become useful when they can do things—query systems of record, trigger workflows, or look up specialized knowledge. Until now that meant hand-rolling Azure Functions, managing OpenAPI specs, or writing custom plug-ins for every backend you own.  MCP changes the economics: it is an open, JSON-RPC–based protocol—originall...