Microsoft Agent Framework
The latest news from the Microsoft Agent Framework team for developers
Latest posts
What’s New in Agent Skills: Code Skills, Script Execution, and Approval for Python
Code-Defined Skills, Script Execution, and Approval for Agent Skills in Python When we introduced Agent Skills for Microsoft Agent Framework, you could package domain expertise as file-based skill directories and have agents discover and load them on demand. Now, the Python SDK takes skills further — you can define skills entirely in code, let agents execute scripts bundled with skills, and gate script execution behind human approval. These additions give you more flexibility in how you author skills, more power in what agents can do with them, and more control over when agents are allowed to act. Code-Defined ...
Agent Harness in Agent Framework
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 agents: Shell and Filesystem Harness Many agent experiences need to do more than generate text. They need to inspect files, run commands, and work with the surrounding environment in a controlled way. Agent Framework makes it possible to model those capabil...
Give Your Agents Domain Expertise with Agent Skills in Microsoft Agent Framework
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 they need, when they need it. What Are Agent Skills? Agent Skills is a simple, open format for giving agents new capabilities and expertise. At the core of every skill is a file — a markdown document that describes what the skill does and provides step-by-step instr...
Migrate your Semantic Kernel and AutoGen projects to Microsoft Agent Framework Release Candidate
We're thrilled to announce that Microsoft Agent Framework has reached Release Candidate status for both .NET and Python. Release Candidate is an important milestone on the road to General Availability — it means the API surface is stable, and all features that we intend to release with version 1.0 are complete. Now is the time to move your Semantic Kernel project to Microsoft Agent Framework and give us your feedback before final release. Whether you're building a single helpful assistant or orchestrating a team of specialized agents, Agent Framework gives you a consistent, multi-language foundation to do it. Wh...
From Local Models to Agent Workflows: Building a Deep Research Solution with Microsoft Agent Framework on Microsoft Foundry Local
Introduction: A New Paradigm for AI Application Development In enterprise AI application development, we often face this dilemma: while cloud-based large language models are powerful, issues such as data privacy, network latency, and cost control make many scenarios difficult to implement. Traditional local small models, although lightweight, lack complete development, evaluation, and orchestration frameworks. The combination of Microsoft Foundry Local and Agent Framework (MAF) provides an elegant solution to this dilemma. This article will guide you from zero to one in building a complete Deep Research agent w...
Build AI Agents with Claude Agent SDK and Microsoft Agent Framework
Microsoft Agent Framework now integrates with the Claude Agent SDK, enabling you to build AI agents powered by Claude's full agentic capabilities. This integration brings together the Agent Framework's consistent agent abstraction with Claude's powerful features, including file editing, code execution, function calling, streaming responses, multi-turn conversations, and Model Context Protocol (MCP) server integration — available in Python. Why Use Agent Framework with Claude Agent SDK? You can use the Claude Agent SDK on its own to build agents. So why use it through Agent Framework? Here are the key reasons: ...
Build AI Agents with GitHub Copilot SDK and Microsoft Agent Framework
Microsoft Agent Framework now integrates with the GitHub Copilot SDK, enabling you to build AI agents powered by GitHub Copilot. This integration brings together the Agent Framework's consistent agent abstraction with GitHub Copilot's capabilities, including function calling, streaming responses, multi-turn conversations, shell command execution, file operations, URL fetching, and Model Context Protocol (MCP) server integration — all available in both .NET and Python. Why Use Agent Framework with GitHub Copilot SDK? You can use the GitHub Copilot SDK on its own to build agents. So why use it through Agent Frame...
The “Golden Triangle” of Agentic Development with Microsoft Agent Framework: AG-UI, DevUI & OpenTelemetry Deep Dive
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 Samples—GHModel.AI—to reveal the "Golden Triangle" development stack that perfectly solves these pain points: DevUI, AG-UI, and OpenTelemetry. Let's explore how this powerful combination empowers the entire local development lifecycle. Phase 1: Creation — Standing on t...
Unlocking Enterprise AI Complexity: Multi-Agent Orchestration with the Microsoft Agent Framework
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 review processes, the fundamental architectural challenge shifts: How do we effectively coordinate and manage a network of specialized, atomic AI capabilities? Much like a high-performing corporation relies on specialized departments, we must transition from a single-execu...
Semantic Kernel and Microsoft Agent Framework
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've had from creating AutoGen and Semantic Kernel. Please give a try and give us your feedback, we think you'll like it! If you've been building and shipping on Semantic Kernel, I'm sure you have questions. I've answered the most common here but, as always, you...
Encoding Changes for Template Arguments in Semantic Kernel
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 will now be thrown when complex types are used as arguments. This enforces more secure template rendering by requiring developers to handle encoding manually for complex types and explicitly disable automatic encoding for those variables. This change promotes best practic...
Azure Authentication Changes in Semantic Kernel Python
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 removed to encourage more secure and explicit authentication practices. If your code relied on this default behavior, you may encounter errors after updating, and you'll need to make minor code adjustments to continue using credential-based authentication. This post expla...
Guest Blog: Building Multi-Agent Solutions with Semantic Kernel and A2A Protocol
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 platforms, frameworks, or even across organizational boundaries. This is where the combination of Microsoft's Semantic Kernel orchestration capabilities and Agent-to-Agent (A2A) protocol creates a powerful foundation for building truly interoperable multi-agent systems. ...
Semantic Kernel Python Gets a Major Vector Store Upgrade
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 workflows. What Makes This Release Special? The new vector store architecture consolidates everything under and delivers three key improvements: Let's explore what makes these changes valuable. Unified Field Model - Simplified Configuration We've repla...
Enhancing Plugin Metadata Management with SemanticPluginForge
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 Calling Feature The function calling feature in LLMs allows developers to define a set of functions that the model can invoke during a conversation. These functions are described using metadata, which includes the function name, parameters, and their descriptions. The LL...
Smarter SK Agents with Contextual Function Selection
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 most relevant functions based on the current conversation context, this feature ensures that your AI agents are smarter, faster, and more effective than ever before. Why Contextual Function Selection Matters When dealing with a large number of available functions, AI mod...
Semantic Kernel and Microsoft.Extensions.AI: Better Together, Part 2
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 Started with Microsoft.Extensions.AI and Semantic Kernel Before we dive into examples, let's understand what we'll be working with. Microsoft.Extensions.AI provides foundational abstractions like and , while Semantic Kernel builds upon these to provide higher-level functio...
Semantic Kernel: Multi-agent Orchestration
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 patterns, their capabilities, and how you can leverage them in your own projects. Why Multi-agent Orchestration? Traditional single-agent systems are limited in their ability to handle complex, multi-faceted tasks. By orchestrating multiple agents, each with special...
Semantic Kernel and Microsoft.Extensions.AI: Better Together, Part 1
This is the start of a series highlighting the integration between Microsoft Semantic Kernel and Microsoft.Extensions.AI. Future parts will provide detailed examples of using Semantic Kernel with Microsoft.Extensions.AI abstractions. The most common questions are: This blog post will address these questions and offer guidance on when and how to use them. First, we will explore what Microsoft Extensions AI is and its relationship with Semantic Kernel. The Evolution of AI Integration in .NET with Microsoft Extensions AI Artificial Intelligence, or AI, is evolving at a rapid pace that many d...
Transitioning to new Extensions AI IEmbeddingGenerator interface
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 Semantic Kernel Semantic Kernel has always aimed to provide a unified way to interact with AI services, including embedding generation. Our initial approach used the interface, which served us well during the experimental phase. However, as the AI landscape has matured, so...
Vector Data Extensions are now Generally Available (GA)
We’re excited to announce the release of Microsoft.Extensions.VectorData.Abstractions, a foundational library providing exchange types and abstractions for vector stores when working with vector data in AI-powered applications. This release is the result of a close collaboration between the Semantic Kernel and .NET teams, combining expertise in AI and developer tooling to deliver a robust, extensible solution for developers. What is Microsoft.Extensions.VectorData.Abstractions? Microsoft.Extensions.VectorData.Abstractions provides shared abstractions and utilities for working with vector data, enabling develope...
Semantic Kernel: Package previews, Graduations & Deprecations
Semantic Kernel: Package Previews, Graduations & Deprecations We are excited to share a summary of recent updates and continuous clean-up efforts across the Semantic Kernel .NET codebase. These changes focus on improving maintainability, aligning with the latest APIs, and ensuring a consistent experience for users. Below you’ll find details on package graduations, deprecations, and a few other improvements. Graduations Spring Cleaning – Deprecations Improvements & Updates These updates are part of our ongoing effort to keep the S...
RC1: Semantic Kernel for Java Agents API
We’re excited to announce the release candidate of the Semantic Kernel for Java Agents API! This marks a major step forward in bringing the power of intelligent agents to Java developers, enabling them to build rich, contextual, and interactive AI experiences using the Semantic Kernel framework. What Are Agents in Semantic Kernel? Agents are intelligent, autonomous components that can reason, plan, and act using natural language. They leverage large language models (LLMs) to interact with users, invoke tools, and maintain context over time. With this API, Java developers can now create agents that: ...
Guest Blog: Orchestrating AI Agents with Semantic Kernel Plugins: A Technical Deep Dive
Today we're excited to welcome Jarre Nejatyab as a guest blog to highlight a technical deep dive on orchestrating AI Agents with Semantic Kernel Plugins. In the rapidly evolving world of Large Language Models (LLMs), orchestrating specialized AI agents has become crucial for building sophisticated cognitive architectures capable of complex reasoning and task execution. While powerful, coordinating multiple agents—each with unique capabilities and data access—presents significant engineering challenges. Microsoft's Semantic Kernel (SK) offers a robust framework for managing this complexity through its intuitive p...
Guest Blog: Letting AI Help Make the World More Accessible – Analyzing Website Accessibility with Semantic Kernel and OmniParser
Today we're excited to welcome Jonathan David, as a guest author on the Semantic Kernel blog. We'll turn it over to Jonathan to dive into Letting AI Help Make the World More Accessible - Analyzing Website Accessibility with Semantic Kernel and OmniParser. With the European Accessibility Act and Germany's Barrierefreiheitsstärkungsgesetz (which translates to Barrier Freedom Strengthening Act) coming into force in July 2025, ensuring digital accessibility is no longer optional. This article explores the importance of accessibility and how AI-driven solutions using Semantic Kernel and OmniParser could strea...
Guest Blog: SemantiClip: A Practical Guide to Building Your Own AI Agent with Semantic Kernel
Today we’re excited to welcome Vic Perdana, as a guest author on the Semantic Kernel blog today to cover his work on a SemantiClip: A Practical Guide to Building Your Own AI Agent with Semantic Kernel. We’ll turn it over to Vic to dive in further. Everywhere you look lately, the buzz is about AI agents. But cutting through the noise—what does agentic AI really mean for developers and builders? How can we move from hype to building real, practical solutions that solve business problems, automate workflows, and, simply put, make our lives easier? I'm excited to share my journey and, as a little Easter egg...
Customer Case Study: Microsoft Store Assistant — bringing multi expert intelligence to Microsoft Store chat with Semantic Kernel and Azure AI
Introduction In October 2024 Microsoft replaced a legacy rule‑based chat bot on Microsoft Store with Microsoft Store Assistant, powered by Azure Open AI, Semantic Kernel, and real‑time page context. The transformation changed a scripted, button-driven experience into a conversation that comprehends the entire public Microsoft portfolio, including Surface and Xbox products, Microsoft 365 subscriptions, Azure services, and the Dynamics and Power Platform portfolio, and knows when to involve a human Sales Associate. Six months later, the assistant manages several millions of conversations annually, maintains a fo...