On the coronary heart of Microsoft’s AI software growth technique is Semantic Kernel, an open supply set of instruments for managing and orchestrating AI prompts. Since its launch as a solution to simplify constructing retrieval-augmented era (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 software for constructing large-scale agentic AI functions. That announcement shaped the premise of Semantic Kernel’s 2025 highway map, with the primary parts already being delivered.
Constructing agentic workflows with Agent Framework
One of many extra necessary new options in Semantic Kernel is Agent Framework, which can quickly transfer out of preview into common availability. This can guarantee a steady, supported set of instruments able to ship production-grade enterprise AI functions. The Agent Framework will type the premise of Semantic Kernel’s deliberate integration with Microsoft Analysis’s AutoGen, together with the discharge of a standard runtime for brokers that’s constructed utilizing each platforms.
The Agent Framework is meant to assist construct functions round agent-like patterns, providing a method 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 ought to be: a method 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 may be regarded as lengthy transactions that work throughout many alternative software APIs and endpoints.
Obtainable 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 by way of dialog, although it’s straightforward sufficient to construct and run brokers that reply to system occasions quite than direct human actions (and so as to add human approval steps as a part of a dynamic workflow). This allows you to concentrate on 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 by way of 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 activity. If a number of brokers must be known as, you should use an Agent Group Chat to handle these inner prompts through the use of Semantic Kernel to work together and cross outcomes between one another. An Agent Group Chat may be dynamic, including and eradicating participant brokers as wanted.
You’re capable of construct on present Semantic Kernel methods, too. For instance, brokers can use present or new plug-ins in addition to name capabilities. 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 may 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 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 growth 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 software frameworks resembling 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. Nonetheless, a step can include an agent if it has well-defined inputs and outputs. Processes can benefit from widespread patterns, and there’s no motive to have capabilities function sequentially—they’ll run asynchronously in parallel, permitting you to have flows that fan out or that depend upon 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 crucial 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 carry the 2 platforms collectively, with AutoGen supporting each .NET and Python, very similar to 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 resembling 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 software and a mannequin consumer. As soon as put in, you may construct a easy agent with a handful of traces of code. Issues get fascinating while you construct a multi-agent software or, as AutoGen calls it, a staff. 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, resembling a consumer proxy, an internet surfer, or an assistant.
You may rapidly add your individual extensions to customise actions throughout the AutoGen layered framework. This gives particular roles for parts of an agent, beginning with the core API that gives instruments for occasion dealing with and messaging, supplying 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 individual code, in addition to instruments for dealing with directions and prompts. Lastly, the Extensions API is the place you may 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 resembling AgentChat. Even so, .NET is probably going the perfect software to construct brokers that run throughout distributed techniques, utilizing its help for .NET Aspire and, by way of that, frameworks like Dapr.
Constructing multi-agent groups in AutoGen Studio
AutoGen Studio is probably essentially the most fascinating half and would work nicely as a part of the Semantic Kernel Visible Studio Code extension. It installs as a neighborhood net software and gives a spot to assemble groups of brokers and extensions, with the intention of developing a multi-agent software while not having to put in writing any further code (although you should use it to edit generated-configuration JSON). It builds on prime of AutoGen’s AgentChat service.
Functions are constructed by dragging parts onto the Studio canvas and including termination circumstances. This final choice is necessary: That is how an agent “is aware of” it has accomplished a activity and must ship outcomes to both a consumer or a calling operate. Brokers may be additional configured by including fashions and extensions, for instance, utilizing an extension to ship a RAG question in opposition to 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 may work with photographs and audio in addition to textual content prompts. Nodes in a staff may be edited so as to add parameters the place obligatory.
Beneath the hood, AutoGen is a declarative agent growth surroundings, with JSON description of the assorted parts that go into making an agent. You may change 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 provides a gallery the place brokers and different parts may be shared with different customers. When you’ve constructed an agent, you may consider it inside Studio’s playground earlier than constructing it into a bigger course of.
Utilizing declarative programming methods to construct agent groups is sensible; typically 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 achieved?
There’s loads but to return 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 type the spine of our companies. Semantic Kernel is beginning to step out into the enterprise IT world. It’ll be fascinating to observe the way it and AutoGen benefit from the talents and information that exist throughout our organizations, past IT and growth groups.
