Building AI Agents with .NET: A Developer’s Guide to Semantic Kernel and Autonomous Systems

The paradigm of software development is shifting towards intelligent, autonomous systems. For developers in the Microsoft ecosystem, building AI agents with .NET has evolved from a niche concept to a mainstream capability, driven by powerful frameworks like Semantic Kernel. This guide explores how .NET developers can leverage large language models (LLMs) and sophisticated orchestration tools to create agentic AI capable of reasoning, planning, and executing complex tasks, transforming modern application development.

The Rise of Agentic AI in the .NET Ecosystem

Agentic AI represents a significant leap beyond simple chatbots or command-line tools. These are autonomous systems designed to understand high-level goals, decompose them into actionable steps, and interact with software environments to achieve them. The growing adoption of .NET for creating these agents is a testament to the platform’s maturity, performance, and robust tooling provided by Microsoft.

The industry is rapidly moving in this direction. According to market projections from IDC, by 2027, “more than 60% of enterprise IT applications will embed agentic AI components for workflow automation,” with Microsoft’s .NET ecosystem positioned as a leading platform for this transformation. This trend is further confirmed by a Forrester survey, which found that “78% of .NET enterprise customers are piloting or actively deploying AI-powered agents in business-critical applications” (Forrester, 2025; IDC, 2025). This surge in adoption, including a reported 150% increase in Semantic Kernel usage over the past year, highlights the enterprise demand for reliable, scalable, and secure AI solutions.

What is Semantic Kernel? The Core of Building AI Agents with .NET

At the heart of the .NET AI agent revolution is Semantic Kernel (SK), an open-source SDK from Microsoft that acts as an AI orchestration layer. It simplifies the integration of LLMs like OpenAI’s GPT-4o or models from Azure AI with conventional C# or F# code. Instead of just sending prompts, Semantic Kernel allows developers to build a sophisticated “brain” for their applications, complete with memory, skills, and planning capabilities.

The official documentation aptly describes its advanced capabilities for agentic systems:

“The Semantic Kernel Agent Framework provides a platform within the Semantic Kernel eco-system that allows for the creation of AI agents… If your application requires entities that can make independent decisions and adapt to changing conditions… agents introduce more autonomy, flexibility, and interactivity into the development process.” — Microsoft Learn, Semantic Kernel Framework

Semantic Kernel achieves this through a modular architecture composed of several key components:

• The Kernel: The central orchestrator that processes requests, manages workflows, and invokes the necessary components.
• Connectors: These modules provide connectivity to various LLMs (both cloud-hosted and local), vector databases for memory, and other external services.
• Plugins (Skills): These are collections of functions that define the agent’s capabilities. A plugin can be a semantic function (a natural language prompt) or a native C# function that interacts with APIs, databases, or local files. This plug-and-play model makes agents highly extensible.
• Planners: Planners are what give an agent its autonomy. Given a complex goal, a planner analyzes the available plugins and creates a step-by-step execution plan for the kernel to follow.

Core Principles of Developing .NET AI Agents

Crafting effective AI agents involves more than just connecting to an LLM. The modern approach, championed by frameworks like Semantic Kernel, is built on several foundational principles that ensure agents are powerful, flexible, and secure.

Seamless LLM Integration

The reasoning power of an AI agent comes from its underlying language model. The .NET ecosystem provides robust connectors to state-of-the-art models like OpenAI’s GPT-4 and GPT-4o, as well as a rich selection of models hosted on Azure AI. A key advantage of using Semantic Kernel is its model-agnostic design. Developers can abstract away the specifics of each model, allowing for greater flexibility.

This flexibility is crucial for optimizing performance, cost, and privacy. As one expert noted on the Microsoft DevBlogs:

“With Semantic Kernel, you can ‘plug & play’ and toggle between local Small Language Models (SLM) and state-of-the-art cloud hosted Large Language Models such as the Azure AI Foundry gpt-4o, per task.” — Guest Blog, Microsoft DevBlogs

This enables a hybrid approach where an agent might use a fast, local SLM for simple data-parsing tasks and a powerful cloud-based LLM for complex reasoning or content generation, all within the same workflow.

Achieving Agent Autonomy through Planning

True agentic behavior is defined by autonomy. A .NET AI agent can independently create and execute multi-step plans to achieve a goal. For example, if a user asks, “Summarize the key points from the latest financial report PDF and email them to the finance team,” a well-designed agent will:

1. Decompose the Goal: The planner identifies the necessary sub-tasks: find the PDF, extract its text, summarize the content, identify the finance team’s email addresses, and draft an email.
2. Select the Right Tools: It matches each sub-task to an available plugin (e.g., a `FileReader` plugin, a `Summarizer` plugin, a `GraphAPI` plugin for emails).
3. Execute the Plan: The kernel executes the sequence of functions, passing the output of one step as the input to the next, until the final objective is met.

This ability to reason and interact with a diverse set of tools, from web APIs to internal databases, is what separates an AI agent from a simple LLM-powered function call. As one publication notes, an AI agent in .NET collaborates with state-of-the-art language models… even executing code snippets or gathering real-time information.

Plug-and-Play Extensibility with Skills

The power of a .NET AI agent is directly proportional to the “skills” it possesses. Semantic Kernel’s plugin architecture makes it incredibly easy to extend an agent’s capabilities. A developer can add new functionalities simply by creating a C# class with methods that perform specific tasks. These can range from simple utilities to complex integrations:

• Web Scraping: A plugin that can fetch and parse content from a URL.
• Database Interaction: A plugin with functions to execute SQL queries and retrieve data.
• File System Operations: A plugin for reading, writing, and modifying local files.
• API Integration: Plugins that interact with third-party APIs like Salesforce, GitHub, or internal LOB applications.

This modular approach allows teams to build a library of reusable skills that can be composed in countless ways by the agent’s planner, promoting code reuse and accelerating development.

Ensuring Enterprise-Grade Security and Governance

As agents become more autonomous and interact with sensitive systems, security becomes paramount. Modern frameworks are being built with enterprise needs in mind. Semantic Kernel includes mechanisms for handling credentials securely, managing permissions for different plugins, and implementing filters to prevent harmful or unintended actions. By embedding security at the framework level, developers can confidently deploy AI agents that interact with critical data and APIs, addressing a key barrier to enterprise adoption (Crispy Code).

A Practical Approach to Building AI Agents with .NET

While the theory is compelling, the practical implementation is surprisingly accessible for .NET developers. Here’s a high-level overview of the development process using Semantic Kernel.

Step 1: Setting Up Your .NET Project and Kernel

The first step is to add the necessary NuGet package to your C# project:

dotnet add package Microsoft.SemanticKernel

Next, you initialize the `Kernel`. This involves configuring it with the desired LLM service, such as Azure OpenAI or a local model.

using Microsoft.SemanticKernel;

// Create a kernel builder
var builder = Kernel.CreateBuilder();

// Add your AI service of choice (e.g., Azure OpenAI)
builder.AddAzureOpenAIChatCompletion(
    deploymentName: "your-gpt4o-deployment-name",
    endpoint: "https://your-resource.openai.azure.com/",
    apiKey: "your-azure-openai-key"
);

// Build the kernel
Kernel kernel = builder.Build();

Step 2: Defining Agent Capabilities with Plugins

Plugins are standard C# classes. Public methods decorated with the `[KernelFunction]` attribute become available to the agent as tools. Here’s a conceptual example of a simple plugin for interacting with a project management tool.

using System.ComponentModel;

using Microsoft.SemanticKernel;

public class ProjectManagementPlugin

{

    [KernelFunction, Description("Gets the status of a specific project.")]

    public string GetProjectStatus([Description("The ID of the project.")] string projectId)

    {

        // In a real application, this would call an API

        if (projectId == "PROJ-123")

        {

            return "In Progress";

        }

        return "Not Found";

    }
    [KernelFunction, Description("Generates a status update email draft.")]

    public string CreateStatusUpdateEmail(

        [Description("The project ID.")] string projectId,

        [Description("The current project status.")] string status