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 approach to simplify constructing retrieval-augmented era (RAG) purposes, 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 purposes. 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 vital new options in Semantic Kernel is Agent Framework, which is able to quickly transfer out of preview into common availability. This can guarantee a steady, supported set of instruments able to ship production-grade enterprise AI purposes. The Agent Framework will kind the premise 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 purposes round agent-like patterns, providing a method so as to add autonomy to purposes and to ship what Microsoft calls “goal-oriented purposes.” This can be a good definition of what trendy agentic AI needs to be: a method 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 information and managing what might be regarded as lengthy transactions that work throughout many various utility 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 through dialog, although it’s simple sufficient to construct and run brokers that reply to system occasions slightly 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 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 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 should 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 might be dynamic, including and eradicating participant brokers as wanted.

You’re in a position to construct on present Semantic Kernel methods, too. For instance, brokers can use present or new plug-ins in addition to name features. Working with exterior purposes 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 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 establishing queries by parsing person 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 resembling 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 reap the benefits of frequent patterns, and there’s no cause to have features function sequentially—they’ll 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 undertaking 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 undertaking, there’s nonetheless some work essential to deliver 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 quite a lot 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 permits 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 if you construct a multi-agent utility or, as AutoGen calls it, a workforce. 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, resembling a person proxy, an online surfer, or an assistant.

You may shortly add your personal extensions to customise actions inside the AutoGen layered framework. This offers 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 will help you shortly construct brokers utilizing prebuilt elements 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 resembling 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 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 internet utility and offers a spot to assemble groups of brokers and extensions, with the purpose of establishing a multi-agent utility without having to jot down any extra code (although you should use it to edit generated-configuration JSON). It builds on high of AutoGen’s AgentChat service.

Functions are constructed by dragging elements onto the Studio canvas and including termination circumstances. This final choice is vital: That is how an agent “is aware of” it has accomplished a activity and must ship outcomes to both a person or a calling operate. Brokers might 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 photos and audio in addition to textual content prompts. Nodes in a workforce might be edited so as to add parameters the place crucial.

Underneath the hood, AutoGen is a declarative agent improvement setting, with JSON description of the varied parts that go into making an agent. You may 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 purposes, it presents a gallery the place brokers and different elements might 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 methods to construct agent groups is smart; usually the data wanted to assemble parts 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 parts of these processes, who higher to design these duties than the individuals who know precisely what must be finished?

There’s loads 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 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 talents and data that exist throughout our organizations, past IT and improvement groups.