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 method 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 fashioned the idea of Semantic Kernel’s 2025 highway map, with the primary components already being delivered.
One of many extra necessary new options in Semantic Kernel is the Agent Framework, which is able to quickly transfer out of preview into basic availability. This may guarantee a steady, supported set of instruments able to ship production-grade enterprise AI functions. The Agent Framework will type 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 method 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 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 could be considered lengthy transactions that work throughout many alternative 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 quite 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 companies.
Brokers speaking to brokers
Constructing a Semantic Kernel agent requires an Agent class earlier than utilizing an Agent Chat to assist interactions between your agent workflow and the AI and API endpoints used to finish the present job. If a number of brokers must be referred to as, you should use an Agent Group Chat to handle these inner prompts by utilizing Semantic Kernel to work together and go outcomes between one another. An Agent Group Chat could be dynamic, including and eradicating participant brokers as wanted.
You’re in a position to construct on current Semantic Kernel methods, too. For instance, brokers can use current 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 mandatory and add plug-in objects. Your plug-ins will handle API calls, with the agent setting up queries by parsing consumer 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 akin 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 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 crucial basis, as Orleans’ transfer from being a Microsoft Analysis challenge 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 assist for multi-agent operations in Semantic Kernel. Because it’s been a analysis challenge, 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 assist for a wide range of totally different LLMs akin to OpenAI (and Azure OpenAI). There’s additionally assist for Ollama, Azure Foundry-hosted fashions, Google Gemini, and a Semantic Kernel adapter that allows you to use Semantic Kernel’s mannequin shoppers.
Getting began with AutoGen
Getting began with AutoGen requires the core AutoGen utility and a mannequin consumer. As soon as put in, you may construct a easy agent with a handful of strains of code. Issues get attention-grabbing once you construct a multi-agent utility or, as AutoGen calls it, a group. 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, akin to a consumer proxy, an internet surfer, or an assistant.
You’ll be able to rapidly add your individual extensions to customise actions throughout the AutoGen layered framework. This gives 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 can assist you rapidly construct brokers utilizing prebuilt elements and your individual code, in addition to instruments for dealing with directions and prompts. Lastly, the Extensions API is the place you may add assist 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 akin to AgentChat. Even so, .NET is probably going the most effective device to construct brokers that run throughout distributed programs, utilizing its assist for .NET Aspire and, via Aspire, frameworks like Dapr.
Constructing brokers in AutoGen Studio
AutoGen Studio is maybe probably the most attention-grabbing half and would work properly as a part of the Semantic Kernel extension for Visible Studio Code. It installs as an area net utility and gives a spot to assemble groups of brokers and extensions, with the purpose of setting up a multi-agent utility while not having to put in writing any extra 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 elements onto the AutoGen Studio canvas and including termination situations. This final choice is necessary: That is how an agent “is aware of” it has accomplished a job and must ship outcomes to both a consumer or a calling operate. Brokers could 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 assist helps you select an acceptable 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 group could be edited so as to add parameters the place mandatory.
Beneath the hood, AutoGen is a declarative agent improvement setting, with JSON description of the assorted components that go into making an agent. You’ll be able to swap to a JSON view to make adjustments 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 elements could 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 smart; typically the information wanted to assemble components 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 components of these processes, who higher to design these duties than the individuals who know precisely what must be accomplished?
There’s quite a bit but to come back for Semantic Kernel in 2025. Now that we’re popping out of the experimental section 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 attention-grabbing to observe the way it and AutoGen reap the benefits of the talents and information that exist throughout our organizations, past IT and improvement groups.
