On the coronary heart of Microsoft’s AI utility growth technique is Semantic Kernel, an open supply set of instruments for managing and orchestrating AI prompts. Since its launch as a strategy 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 popular device for constructing large-scale agentic AI functions. That announcement shaped the idea of Semantic Kernel’s 2025 street map, with the primary parts already being delivered.
One of many extra vital new options in Semantic Kernel is the Agent Framework, which can quickly transfer out of preview into common availability. This can 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.
Constructing agentic workflows with the Agent Framework
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.” This can be a good definition of what trendy agentic AI needs to be: a means of utilizing AI instruments to assemble and handle a workflow based mostly on a person request. It then permits a number of brokers to collaborate, sharing knowledge and managing what may be considered lengthy transactions that work throughout many alternative utility APIs and endpoints.
Out there 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 straightforward 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 give attention to 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 at all times, 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 related companies.
Brokers speaking to brokers
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 have to 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 go outcomes between one another. An Agent Group Chat may be dynamic, including and eradicating participant brokers as wanted.
You’re in a position to construct on current Semantic Kernel strategies, too. For instance, brokers can use current or new plug-ins in addition to name features. Working with exterior functions is essential 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 massive language mannequin (LLM) you’re utilizing, in addition to management entry to the APIs. Your code can handle authorization as crucial and add plug-in objects. Your plug-ins will handle API calls, with the agent developing queries by parsing person inputs.
No-code brokers 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 comparable to Dapr and Orleans. Workflows are event-driven, with steps constructed round Semantic Kernel Capabilities. 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 reap the benefits of widespread patterns, and there’s no motive to have features function sequentially—they will run asynchronously in parallel, permitting you to have flows that fan out or that rely upon a number of inputs.
The 2 platforms converge of their use of Orleans, which ensures they’ve related approaches to working in event-driven environments. This is a vital basis, as Orleans’ transfer from being a Microsoft Analysis venture 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 venture, there’s nonetheless some work essential to convey the 2 platforms collectively, with AutoGen supporting each .NET and Python, very like Semantic Kernel.
Actually AutoGen simplifies the method of constructing brokers, with a no-code GUI and help for a wide range of completely different LLMs comparable to OpenAI (and Azure OpenAI). There’s additionally help for Ollama, Azure Foundry-hosted fashions, Google Gemini, and a Semantic Kernel adapter that allows you to use Semantic Kernel’s mannequin purchasers.
Getting began with AutoGen
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 traces of code. Issues get fascinating whenever you construct a multi-agent utility or, as AutoGen calls it, a group. Groups are introduced collectively in a bunch chat the place customers give brokers duties. It comes with prebuilt brokers that can be utilized as constructing blocks, comparable to a person proxy, an internet surfer, or an assistant.
You’ll be able to rapidly add your personal extensions to customise actions inside the AutoGen layered framework. This supplies particular roles for parts 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 can assist you rapidly construct brokers utilizing prebuilt parts and your personal 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 personal code.
A lot of the documentation focuses on Python. Though there’s a .NET implementation of AutoGen, it’s lacking documentation for key options comparable to AgentChat. Even so, .NET is probably going the perfect device to construct brokers that run throughout distributed programs, utilizing its help for .NET Aspire and, via Aspire, frameworks like Dapr.
Constructing brokers in AutoGen Studio
AutoGen Studio is maybe probably the most fascinating half and would work nicely as a part of the Semantic Kernel extension for Visible Studio Code. It installs as an area internet utility and supplies a spot to assemble groups of brokers and extensions, with the intention of developing a multi-agent utility while not having to write down any extra code (although you should utilize it to edit generated-configuration JSON). It builds on prime of AutoGen’s AgentChat service.
Functions are constructed by dragging parts onto the AutoGen Studio canvas and including termination situations. This final choice is vital: That is how an agent “is aware of” it has accomplished a process and must ship outcomes to both a person or a calling operate. Brokers may be additional configured by including fashions and extensions, for instance, utilizing an extension to ship a RAG question towards enterprise knowledge. A number of mannequin help helps you select an acceptable AI mannequin for an agent, maybe one which’s been fine-tuned or that provides multi-model actions so you’ll be able to work with photographs and audio in addition to textual content prompts. Nodes in a group may be edited so as to add parameters the place crucial.
Beneath the hood, AutoGen is a declarative agent growth setting, with JSON description of the assorted parts that go into making an agent. You’ll be able to 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 affords a gallery the place brokers and different parts may 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 sensible; usually the information wanted to assemble parts of a workflow or enterprise course of is embedded within the course of itself as information passes from employee to employee. If we’re to construct AI-based brokers to automate parts of these processes, who higher to design these duties than the individuals who know precisely what must be completed?
There’s loads 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 learn 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 reap the benefits of the abilities and information that exist throughout our organizations, past IT and growth groups.
