{"id":4494,"date":"2025-03-13T14:39:54","date_gmt":"2025-03-13T21:39:54","guid":{"rendered":"https:\/\/devblogs.microsoft.com\/semantic-kernel\/?p=4494"},"modified":"2025-03-13T14:39:54","modified_gmt":"2025-03-13T21:39:54","slug":"the-future-of-ai-customizing-ai-agents-with-the-semantic-kernel-agent-framework","status":"publish","type":"post","link":"https:\/\/devblogs.microsoft.com\/agent-framework\/the-future-of-ai-customizing-ai-agents-with-the-semantic-kernel-agent-framework\/","title":{"rendered":"The Future of AI: Customizing AI Agents with the Semantic Kernel Agent Framework"},"content":{"rendered":"

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<\/a><\/p>\n

<\/p>\n

\n
\n
\n
\n
\n
\n

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:\u202f<\/span>https:\/\/aka.ms\/the-future-of-ai<\/span><\/a>\u00a0<\/span><\/em><\/p>\n

Customizing AI agents with the Semantic Kernel agent framework<\/h1>\n

AI agents are autonomous entities designed to solve complex tasks for humans. Compared to traditional software agents, AI-powered agents allow for more robust solutions with less coding. Individual AI agents have shown significant capabilities, achieving results previously not possible. The potential of these agents is enhanced when multiple specialized agents collaborate within a multi-agent system. Research has shown that such systems, comprising single-purpose agents, are more effective than single multi-purpose agents in many tasks\u00a0[1]<\/a>. This enables automation of more complex workflows with improved results and higher efficiency in the future.<\/p>\n

In this post, we are going to explore how you can build single agents and multi-agent systems with Semantic Kernel.<\/p>\n

Semantic Kernel<\/span><\/a>\u00a0is a lightweight and open-source SDK developed by Microsoft, designed to facilitate the creation of production-ready AI solutions. Despite its capabilities, Semantic Kernel remains accessible, allowing developers to start with minimal code. For scalable deployment, it offers advanced features such as telemetry, hooks, and filters to ensure the delivery of secure and responsible AI solutions.<\/span>\u00a0<\/span><\/p>\n

The Semantic Kernel Agent Framework offers pro-code orchestration within the Semantic Kernel ecosystem, facilitating the development of AI agents and agentic patterns capable of addressing more complex tasks autonomously.\u00a0<\/span><\/p>\n

Starting with individual agents is recommended. Semantic Kernel provides a variety of AI service connectors, allowing developers and companies to select models from different providers or even local models. Additionally, Semantic Kernel gives developers the flexibility to integrate their agents created from managed services like\u00a0Azure OpenAI Service<\/a>\u00a0Assistant API and\u00a0Azure AI Agent Service<\/a>\u00a0into a unified system.\u00a0<\/span>R<\/span>efer to the samples in the Semantic Kernel GitHub repository to get you started.<\/span>\u00a0<\/span><\/p>\n

    \n
  • Python:\u00a0<\/span>semantic-kernel\/python\/samples\/getting_started_with_agents at main \u00b7 microsoft\/semantic-kernel<\/span><\/a>\u00a0<\/span><\/li>\n
  • .Net:\u00a0<\/span>semantic-kernel\/dotnet\/samples\/GettingStartedWithAgents at main \u00b7 microsoft\/semantic-kernel<\/span><\/a>\u00a0<\/span><\/li>\n<\/ul>\n

    Previous posts have thoroughly examined the principles of designing single agents and the effectiveness of multi-agent systems. The objective of this post is not to determine when a single agent should be employed versus a multi-agent system;\u00a0however, it is important to emphasize that agents should be designed with a single purpose to maximize their performance. Assigning multiple responsibilities or capabilities to a single agent is likely to result in suboptimal outcomes.<\/span><\/p>\n

    If your tasks can be efficiently accomplished by a single agent, that\u2019s great! If you find that the performance of a single agent is unsatisfactory, you might consider employing multiple agents to collaboratively address your tasks. Our recent\u00a0Microsoft Mechanics video<\/a>\u00a0outlines how a multi-agent system operates.\u00a0<\/span><\/p>\n