On the coronary heart of Microsoft’s AI utility improvement technique is Semantic Kernel, an open supply set of instruments for managing and orchestrating AI prompts. Since its launch as a option to simplify constructing retrieval-augmented technology (RAG) functions, it has grown right into a framework for constructing and managing agentic AI.
At Ignite in 2024, Microsoft introduced a number of new options for Semantic Kernel, positioning it as its most well-liked device for constructing large-scale agentic AI functions. That announcement shaped the idea of Semantic Kernel’s 2025 street map, with the primary components already being delivered.
Constructing agentic workflows with Agent Framework
One of many extra essential new options in Semantic Kernel is Agent Framework, which is able to quickly transfer out of preview into basic availability. It will guarantee a secure, supported set of instruments able to ship production-grade enterprise AI functions. The Agent Framework will kind the idea of Semantic Kernel’s deliberate integration with Microsoft Analysis’s AutoGen, together with the discharge of a typical runtime for brokers that’s constructed utilizing each platforms.
The Agent Framework is meant to assist construct functions round agent-like patterns, providing a means so as to add autonomy to functions and to ship what Microsoft calls “goal-oriented functions.” It is a good definition of what fashionable agentic AI must be: a means of utilizing AI instruments to assemble and handle a workflow based mostly on a consumer request. It then permits a number of brokers to collaborate, sharing information and managing what could be regarded as lengthy transactions that work throughout many various utility APIs and endpoints.
Accessible as an extension to the bottom Semantic Kernel, the Agent Framework is delivered as a set of .NET libraries, which assist handle human/agent interactions and supply entry to OpenAI’s Assistant API. It’s meant to be managed through dialog, although it’s simple sufficient to construct and run brokers that reply to system occasions moderately than direct human actions (and so as to add human approval steps as a part of a dynamic workflow). This allows you to deal with utilizing brokers to handle duties.
Semantic Kernel’s agent options are designed to increase the ideas and instruments used to construct RAG-powered AI workflows. As all the time, Semantic Kernel is how each the general orchestration and particular person brokers run, managing context and state in addition to dealing with calls to AI endpoints through Azure AI Foundry and comparable providers.
Constructing a Semantic Kernel agent requires an Agent class earlier than utilizing an Agent Chat to help interactions between your agent workflow and the AI and API endpoints used to finish the present process. If a number of brokers must be referred to as, you should utilize an Agent Group Chat to handle these inner prompts through the use of Semantic Kernel to work together and move outcomes between one another. An Agent Group Chat could be dynamic, including and eradicating participant brokers as wanted.
You’re capable of construct on current Semantic Kernel strategies, too. For instance, brokers can use current or new plug-ins in addition to name capabilities. Working with exterior functions is vital to constructing enterprise brokers, as they want to have the ability to dynamically generate workflows round each people and software program.
Having Semantic Kernel handle brokers ensures you’ll be able to handle each directions and prompts for the giant language mannequin (LLM) you’re utilizing, in addition to management entry to the APIs. Your code can handle authorization as obligatory and add plug-in objects. Your plug-ins will handle API calls, with the agent developing queries by parsing consumer inputs.
No-code agent improvement with AutoGen
Semantic Kernel’s integration with AutoGen builds on its Course of Framework. That is designed to handle long-running enterprise processes and works with distributed utility frameworks corresponding to Dapr and Orleans. Workflows are event-driven, with steps constructed round Semantic Kernel Features. A course of isn’t an agent, because it’s an outlined workflow and there’s no self-orchestration. Nevertheless, a step can comprise an agent if it has well-defined inputs and outputs. Processes can make the most of frequent patterns, and there’s no motive to have capabilities function sequentially—they will run asynchronously in parallel, permitting you to have flows that fan out or that rely on a number of inputs.
The 2 platforms converge of their use of Orleans, which ensures they’ve comparable approaches to working in event-driven environments. This is a vital basis, as Orleans’ transfer from being a Microsoft Analysis mission to being the foundational distributed computing structure for contemporary .NET has been key to wider uptake.
Utilizing AutoGen as a part of its agent tooling will assist ship higher help for multi-agent operations in Semantic Kernel. Because it’s been a analysis mission, there’s nonetheless some work essential to convey the 2 platforms collectively, with AutoGen supporting each .NET and Python, very like Semantic Kernel.
Definitely AutoGen simplifies the method of constructing brokers, with a no-code GUI and help for quite a lot of completely different LLMs corresponding to OpenAI (and Azure OpenAI). There’s additionally help for Ollama, Azure Foundry-hosted fashions, Gemini, and a Semantic Kernel adapter that allows you to use Sematic Kernel’s mannequin shoppers.
Getting began with AutoGen requires the core AutoGen utility and a mannequin shopper. As soon as put in, you’ll be able to construct a easy agent with a handful of strains of code. Issues get fascinating once you construct a multi-agent utility or, as AutoGen calls it, a staff. Groups are introduced collectively in a gaggle chat the place customers give brokers duties. It comes with prebuilt brokers that can be utilized as constructing blocks, corresponding to a consumer proxy, an online surfer, or an assistant.
You may rapidly add your individual extensions to customise actions inside the AutoGen layered framework. This offers particular roles for components of an agent, beginning with the core API that gives instruments for occasion dealing with and messaging, providing you with an asynchronous hub for agent operations. Above that’s the AgentChat API. That is designed that will help you rapidly construct brokers utilizing prebuilt parts and your individual code, in addition to instruments for dealing with directions and prompts. Lastly, the Extensions API is the place you’ll be able to add help for each new LLMs and your individual code.
A lot of the documentation focuses on Python. Though there’s a .NET implementation of AutoGen, it’s lacking documentation for key options corresponding to AgentChat. Even so, .NET is probably going the most effective device to construct brokers that run throughout distributed programs, utilizing its help for .NET Aspire and, by means of that, frameworks like Dapr.
Constructing multi-agent groups in AutoGen Studio
AutoGen Studio is probably probably the most fascinating half and would work properly as a part of the Semantic Kernel Visible Studio Code extension. It installs as an area internet utility and offers a spot to assemble groups of brokers and extensions, with the intention of developing a multi-agent utility without having to jot down any extra code (although you should utilize it to edit generated-configuration JSON). It builds on high of AutoGen’s AgentChat service.
Functions are constructed by dragging parts onto the Studio canvas and including termination circumstances. This final choice is essential: That is how an agent “is aware of” it has accomplished a process and must ship outcomes to both a consumer or a calling perform. Brokers could be additional configured by including fashions and extensions, for instance, utilizing an extension to ship a RAG question towards enterprise information. A number of mannequin help helps you select an appropriate AI mannequin for an agent, maybe one which’s been fine-tuned or that gives multi-model actions so you’ll be able to work with pictures and audio in addition to textual content prompts. Nodes in a staff could be edited so as to add parameters the place obligatory.
Underneath the hood, AutoGen is a declarative agent improvement setting, with JSON description of the varied components that go into making an agent. You may swap to a JSON view to make modifications and even convert AutoGen AgentChat Python to JSON and edit it in Studio. To simplify constructing new functions, it presents a gallery the place brokers and different parts could be shared with different customers. When you’ve constructed an agent, you’ll be able to consider it inside Studio’s playground earlier than constructing it into a bigger course of.
Utilizing declarative programming strategies to construct agent groups is smart; usually the data wanted to assemble components of a workflow or enterprise course of is embedded within the course of itself as data passes from employee to employee. If we’re to construct AI-based brokers to automate components of these processes, who higher to design these duties than the individuals who know precisely what must be carried out?
There’s so much but to come back for Semantic Kernel in 2025. Now that we’re popping out of the experimental part of enterprise AI the place we used chatbots to discover ways to construct efficient prompts, it’s time to make use of these classes to construct workflow instruments extra suited to the multi-channel, multi-event processes that kind the spine of our companies. Semantic Kernel is beginning to step out into the enterprise IT world. It’ll be fascinating to look at the way it and AutoGen make the most of the talents and data that exist throughout our organizations, past IT and improvement groups.
