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 option 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 fashioned 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 secure, supported set of instruments able to ship production-grade enterprise AI functions. 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 functions round agent-like patterns, providing a manner so as to add autonomy to functions and to ship what Microsoft calls “goal-oriented functions.” It is a good definition of what trendy agentic AI must be: a manner of utilizing AI instruments to assemble and handle a workflow primarily based 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 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 supposed 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 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 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 assist 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 inside prompts by utilizing Semantic Kernel to work together and cross 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 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 possibly can 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 needed and add plug-in objects. Your plug-ins will handle API calls, with the agent establishing 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 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 benefit from widespread patterns, and there’s no purpose 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 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 assist for multi-agent operations in Semantic Kernel. Because it’s been a analysis undertaking, there’s nonetheless some work essential to carry 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 quite a lot of totally different LLMs corresponding to OpenAI (and Azure OpenAI). There’s additionally assist for Ollama, Azure Foundry-hosted fashions, Gemini, and a Semantic Kernel adapter that permits you to use Sematic Kernel’s mannequin purchasers.

Getting began with AutoGen requires the core AutoGen utility and a mannequin shopper. As soon as put in, you possibly can construct a easy agent with a handful of strains of code. Issues get attention-grabbing whenever 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, corresponding to a consumer proxy, an internet surfer, or an assistant.

You’ll be able to rapidly add your individual 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 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 possibly can 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 corresponding to AgentChat. Even so, .NET is probably going the perfect software to construct brokers that run throughout distributed programs, utilizing its assist for .NET Aspire and, via that, frameworks like Dapr.

Constructing multi-agent groups in AutoGen Studio

AutoGen Studio is probably probably the most attention-grabbing half and would work properly as a part of the Semantic Kernel Visible Studio Code extension. It installs as an area net utility and supplies a spot to assemble groups of brokers and extensions, with the purpose of establishing a multi-agent utility with no need to put in writing any extra code (although you should use it to edit generated-configuration JSON). It builds on high of AutoGen’s AgentChat service.

Purposes 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 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 assist 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 possibly can 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 needed.

Below 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 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 affords a gallery the place brokers and different parts could be shared with different customers. When you’ve constructed an agent, you possibly can 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 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 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 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 attention-grabbing to observe the way it and AutoGen benefit from the talents and information that exist throughout our organizations, past IT and growth groups.