Showing archive results for 2025

In the explosive era of Agentic AI, we're not just seeking more powerful models—we're searching for a development experience that lets developers actually get some sleep. When building Agents locally, we've traditionally faced three major challenges: Today, I'll walk you through a classic case from Microsoft Agent Framework Sampl...

The Architectural Imperative: Why Multi-Agent Orchestration is Essential In modern enterprise AI systems, the scope and complexity of real-world business challenges quickly exceed the capabilities of a single, monolithic AI Agent. Facing tasks like end-to-end customer journey management, multi-source data governance, or deep human-in-the-loop revi...

Last week we announced Microsoft Agent Framework, you can find all the details: I'm immensely proud of the work the team that brought you AutoGen and Semantic Kernel have done to create Microsoft Agent Framework. We really think it's a great step forward in building AI agents and applications, building on all the learnings we'v...

In previous versions of the Semantic Kernel, the encoding of template arguments was performed automatically if the argument type was a . The encoding was not applied for custom types, anonymous types, or collections. With the latest changes, we've introduced stricter rules: if automatic encoding is enabled (the default behavior), an exception wi...

In previous versions of the Semantic Kernel Python, the default fallback authentication mechanism for Azure services like was from the Azure Identity library. This provided a convenient way to authenticate without explicitly passing credentials, especially during development. However, with the latest package version , this fallback is being remov...

In the rapidly evolving landscape of AI application development, the ability to orchestrate multiple intelligent agents has become crucial for building sophisticated, enterprise-grade solutions. While individual AI agents excel at specific tasks, complex business scenarios often require coordination between specialized agents running on different p...

We're excited to announce a significant update to Semantic Kernel Python's vector store implementation. Version 1.34 brings a complete overhaul that makes working with vector data simpler, more intuitive, and more powerful. This update consolidates the API, improves developer experience, and adds new capabilities that streamline AI development work...

In the world of software development, flexibility and adaptability are key. Developers often face challenges when it comes to updating plugin metadata dynamically without disrupting services or requiring redeployment. This is where SemanticPluginForge, an open-source project, steps in to improve the way we manage plugin metadata. LLM Function Ca...

Smarter SK Agents with Contextual Function Selection In today's fast-paced AI landscape, developers are constantly seeking ways to make AI interactions more efficient and relevant. The new Contextual Function Selection feature in the Semantic Kernel Agent Framework is here to address this need. By dynamically selecting and advertising only the mos...

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...