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...

Multi-agent systems demo beautifully. Putting them in front of real users is another story. In early prototypes, a terminal or a basic chat window is enough. But once agents start handing off to each other, pausing for approvals, or asking follow-up questions, those interfaces fall apart. Which agent is active? Why is the system waitin...

Google's Agent-to-Agent (A2A) protocol is designed to enable seamless interoperability among diverse AI agents. Microsoft’s Semantic Kernel (SK), an open-source platform for orchestrating intelligent agent interactions, is now being integrated into the A2A ecosystem. In this blog, we demonstrate how Semantic Kernel agents can easily function as an ...

Today we’re excited to dive into Semantic Kernel and Azure AI Agents. There are additional details about using an within Semantic Kernel covered in our documentation here. Azure AI Agents are powerful tools for developers seeking to integrate AI capabilities into their applications. In this blog post, we'll explore how to utilize Azure AI Agent...

In the dynamic field of conversational AI, managing coherent and contextually meaningful interactions between humans and digital assistants poses increasingly complex challenges. As dialogue lengths extend, maintaining full conversational context becomes problematic due to token constraints and memory limitations inherent to large language mo...

We are thrilled to announce the General Availability (GA) release of the OpenAPI package for Semantic Kernel, available for both .NET and Python! With this significant milestone, developers can easily integrate existing APIs as plugins, enhancing the capabilities of their AI agents while making them more versatile in real-world applications. Why O...

The Semantic Kernel Agent Framework revolutionizes how developers can interact with Large Language Models (LLMs) by embedding dynamic, multi-step agents into their applications. By combining the power of LLMs with structured programming, the framework allows developers to build intelligent systems that can autonomously carry out tasks, reason based...

In working with AI applications, ensuring that the output generated by a language model is structured and follows a consistent format is critical—especially when handling complex tasks like solving math problems. A powerful way to achieve this is through the use of JSON Schema, which allows the AI model to produce outputs that align with a predefin...

One of the most exciting features available in certain Large Language Models (LLMs) is function-calling. In Semantic Kernel, we handle the heavy lifting so that you can bring your own code or utilize built-in plugins that cater to your use case. Our goal is to make it easy for you to incorporate function calling into your application. Today, we'll ...

The term "agent" has become a popular term within the industry. There have many different definitions, but at their core, they consist of a system prompt (i.e., a persona), plugins, and an ability to automatically reason and create plans to address user goals. Up until today, we've demonstrated how you could use components of Semantic Kernel to ...