Showing category results for Agents

Your Python agents can now pick up reusable bundles of domain expertise (instructions, reference material, and scripts that load only when a task calls for them) through a stable, production-ready API. Agent Skills for Python in Microsoft Agent Framework is stable and shipping: the core skills API has no experimental gate, so you can rely on it...

You can now give your .NET agents reusable packages of domain expertise - instructions, reference documents, and scripts they load only when a task needs them - through a stable, production-ready API. Agent Skills for .NET in Microsoft Agent Framework has moved out of experimental preview - the attribute is removed and the API is stable. Teams can...

Modern AI agents often aren't bottlenecked by model quality, they are bottlenecked by orchestration overhead. When an agent chains together many small tool calls, each step typically requires a new model turn, driving up latency and token usage. With CodeAct support in Agent Framework, agents can collapse those multi-step plans into a sing...

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 age...

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...

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...

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...

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 ...