Semantic Kernel
The latest news from the Semantic Kernel team for developers
Latest posts
Semantic Kernel Agent Framework RC2
Three weeks ago we released the Release the Agents! SK Agents Framework RC1 | Semantic Kernel and we’ve been thrilled to see the momentum grow. Thank you to everyone who has shared feedback, filed issues, and started building with agents in Semantic Kernel—we’re seeing more developers try agents than ever before. Today, we’re declaring build 1.43 (.NET) and 1.26.1 (Python) as Release Candidate 2 of the Semantic Kernel Agent Framework. With this release, we’re introducing a small but impactful change to how agents handle chat message threads —one that sets the stage for powerful new capabilities coming soon. ...
Accelerating Agentic Workflows with NVIDIA AgentIQ, Azure AI Foundry and Semantic Kernel
Today, we're excited to announce our collaboration with NVIDIA. In Azure AI Foundry, we've integrated NVIDIA NIM microservices and the NVIDIA AgentIQ toolkit into Azure AI Foundry—unlocking unprecedented efficiency, performance, and cost optimization for your AI projects. Read more on the announcement here. Optimizing performance with NVIDIA AgentIQ and Semantic Kernel Once your NVIDIA NIM microservices are deployed, NVIDIA AgentIQ takes center stage. This open-source toolkit is designed to seamlessly connect, profile, and optimize teams of AI agents, enables your systems to run at peak performance. AgentIQ del...
Guest Blog: Build a Multi-Agent System Using Microsoft Azure AI Agent Service and Semantic Kernel in 3 Simple Steps!
Build a Multi-Agent System Using Microsoft Azure AI Agent Service and Semantic Kernel in 3 Simple Steps! Today we're thrilled to welcome back guest author, Akshay Kokane to share his recent Medium article on Build a Multi-Agent System Using Microsoft Azure AI Agent Service and Semantic Kernel in 3 Simple Steps. We’ll turn it over to him to dive in! In my previous blog, I introduced Microsoft’s Azure AI Agent Service, a fully managed platform that simplifies the process of building, deploying, and scaling AI agents. Unlike OpenAI Assistant, Azure AI Agent Service offers greater flexibility, supporting multip...
Customer Case Study: Announcing the Microsoft Semantic Kernel Couchbase Connector
We're thrilled to announce the launch of the Semantic Kernel Couchbase Vector Store Connector for .NET developers, created through our strategic partnership with Microsoft's Semantic Kernel team. This powerful out-of-the-box connector transforms how developers integrate vector search capabilities into their AI applications. What sets this connector apart is how it harnesses Couchbase's distributed NoSQL platform alongside Semantic Kernel's vector store abstractions, creating an integration that prioritizes both performance and developer experience. The Semantic Kernel Couchbase Vector Store Connector elimin...
The Future of AI: Customizing AI Agents with the Semantic Kernel Agent Framework
Today we're excited to promote a recent blog from AI Platform focused on customizing AI agents with the Semantic Kernel agent framework. Read the entire blog post: here The Future of AI blog series is an evolving collection of posts from the AI Futures team in collaboration with subject matter experts across Microsoft. In this series, we explore tools and technologies that will drive the next generation of AI. Explore more at: https://aka.ms/the-future-of-ai Customizing AI agents with the Semantic Kernel agent framework AI agents are autonomous entities designed to solve complex tasks for humans...
Using Azure AI Agents with Semantic Kernel in .NET and Python
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 Agents alongside the Semantic Kernel in both .NET and Python, showcasing the potential of these technologies to create intelligent and responsive applications. What is an Azure AI Agent? An Azure AI Agent is a specialized agent within the Semantic Kernel framework, designe...
Customer Case Story: Creating a Semantic Kernel Agent for Automated GitHub Code Reviews
Today I want to welcome a guest author to our Semantic Kernel blog, Rasmus Wulff Jensen, to cover how he's created a Semantic Kernel agent for automated GitHub code review. We'll turn it over to Rasmus to dive in. Introduction If you work in software development, you know that Code reviews are an essential part of the software development process, ensuring quality, security, and maintainability. However, they also consume a significant amount of developers’ time, often delaying feature delivery and increasing workload. Recognizing this challenge, the company Relewise, a Search and Recommendation SASS Business, ...
Customer Case Study: INCM transforms legal accessibility with an AI Search Assistant
Customer Case Study: INCM transforms legal accessibility with an AI Search Assistant The Imprensa Nacional-Casa da Moeda (INCM) is responsible for managing and publishing Portugal’s Diário da República (Official Gazette of the Republic of Portugal), which includes essential information for understanding laws, regulations, and legal processes. The quantity of information and the complex language used in these documents present significant challenges for individuals seeking to access and understand them. This complexity often acts as an obstacle, limiting access to vital legal information. These challenges not onl...
Keeping the Conversation Flowing: Managing Context with Semantic Kernel Python
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 models (LLMs). These constraints not only degrade conversational clarity but also compromise the system's ability to deliver accurate and relevant responses. Thus, effective solutions require strategies that intelligently balance context retention with efficient memory ma...