Showing tag results for .NET

Developers increasingly want to build agents that can reason about code, modify files, execute commands, interact with developer tools, and work across entire repositories. While GitHub Copilot already provides a powerful coding harness for these scenarios, developers often need additional capabilities such as observability, middleware, approval wo...

Your agents can now discover and load Agent Skills directly from a Model Context Protocol (MCP) server. Instead of shipping every skill inside your application or copying skill folders into each deployment, you point an agent at an MCP server and it pulls the skills it needs on demand. A central team can publish skills once, and every agent across ...

Most multi-agent apps wire every flow in application code: the sequence of steps, branching, and handoffs between agents all live inside the program, making the orchestration harder to review, version, and change. Declarative workflows make that orchestration explicit. In YAML, you define how agents coordinate, how state changes, where exe...

Python's package is now 1.0.0. That puts Microsoft Agent Framework's orchestration layer at 1.0 across Python and .NET. Sequential, concurrent, group chat, handoff, and magentic orchestration are now stable in both SDKs. You can pick the coordination pattern that fits your problem instead of choosing around SDK maturity. Magentic is the best...

You can now give your .NET agents reusable packages of domain expertise - instructions, reference documents, and scripts they load only when a task needs them - through a stable, production-ready API. Agent Skills for .NET in Microsoft Agent Framework has moved out of experimental preview - the attribute is removed and the API is stable. Teams can...

You can now equip your Microsoft Agent Framework agents with portable, reusable skill packages that provide domain expertise on demand – without changing a single line of your agent's core instructions. With built-in skills providers for both .NET and Python, your agents can discover and load Agent Skills at runtime, pulling in only the context the...

This is Part 2 of our series on integrating Microsoft.Extensions.AI with Semantic Kernel. In Part 1, we explored the relationship between these technologies and how they complement each other. Now, let's dive into practical examples showing how to use Microsoft.Extensions.AI abstractions with Semantic Kernel in non-agent scenarios. Getting Start...

As Semantic Kernel shifts its foundational abstractions to Microsoft.Extensions.AI, we are obsoleting and moving away from our experimental embeddings interfaces to the new standardized abstractions that provide a more consistent and powerful way to work with AI services across the .NET ecosystem. The Evolution of Embedding Generation in Seman...

Today we’re thrilled to announce support for Hybrid search with Semantic Kernel Vector Stores for .NET. What is Hybrid Search? Hybrid search performs two parallel searches on a vector database.  The union of the results of these two searches are then returned to callers with a combined rank, based on the rankings from each of the constituent sear...

OpenAI's gpt-4o-audio-preview is a powerful multimodal model that enables audio input and output capabilities, allowing developers to create more natural and accessible AI interactions. This model supports both speech-to-text and text-to-speech functionalities in a single API call through the Chat Completions API, making it suitable for building vo...