Agent harness is the layer where model reasoning connects to real execution: shell and filesystem access, approval flows, and context management across long-running sessions. With Agent Framework, these patterns can now be built consistently in both Python and .NET. In this post, we’ll look at three practical building blocks for production agent...

The field of AI is rapidly evolving, and the need for more sophisticated, collaborative, and flexible agent-based systems is growing. With this in mind, Semantic Kernel introduces a new multi-agent orchestration framework that enables developers to build, manage, and scale complex agent workflows with ease. This post explores the new orchestration ...

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...

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...

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 ...

Throughout our time building Semantic Kernel and working with customers, we've introduced agents and have just started to explore the potential of autonomous AI agents. While the community is in the midst of exploring various architectures for these agents, one source we can draw inspiration from is the microservice architecture. Consider the be...

Chat Copilot Release 0.5 introduces the integration of Semantic Memory (SM).  Chat Copilot now utilizes the ISemanticMemoryClient interface from SM instead of the ISemanticTextMemory interface associated with Semantic Kernel memory connectors. Semantic Memory (SM) is an open-source platform specialized in the efficient indexing of datasets t...

We've heard from many in the community who want to use Semantic Kernel to query their relational database using natural language expressions. We are excited to share this sandbox that enables you explore the capabilities of LLM to generate SQL queries (or SELECT statements): NL2SQL. This has been an area of interest for years (WikiSQL, Spider, et...