Semantic Kernel and .NET: Building AI Agents with Microsoft Technologies

For enterprises building Agentic AI solutions, the choice of framework is a critical architectural decision. It determines how quickly you can develop, how reliably your agents perform in production, and how well your AI systems integrate with your existing technology stack. Microsoft Semantic Kernel has emerged as the leading framework for building AI agents in the .NET ecosystem, and for good reason.

In this article, we explore what Semantic Kernel is, why it is particularly well-suited for enterprise AI development, the architecture patterns it enables, and how it compares to alternative frameworks.

What is Semantic Kernel?

Semantic Kernel is an open-source SDK from Microsoft that enables developers to build AI agents and integrate large language models (LLMs) into applications. Written primarily in C# with Python and Java versions available, it provides the abstractions and building blocks needed to create sophisticated AI systems without reinventing the wheel.

At its core, Semantic Kernel acts as the orchestration layer between your application code, AI models, and external tools. It handles the complexities of prompt management, function calling, planning, memory, and model interaction so that developers can focus on business logic.

Key capabilities include:

• Plugin architecture: Define reusable capabilities (tools, functions, APIs) that agents can discover and use
• AI connectors: Native integration with Azure OpenAI, OpenAI, Hugging Face, and other model providers
• Planners: Built-in planning strategies that allow agents to decompose complex goals into executable steps
• Memory and embeddings: Support for vector stores and semantic memory for RAG implementations
• Agent framework: First-class support for single-agent and multi-agent patterns
• Filters and middleware: Extensible pipeline for logging, content safety, retry logic, and custom processing

Why Semantic Kernel for Enterprise AI?

Several characteristics make Semantic Kernel the natural choice for enterprises, particularly those already invested in the Microsoft ecosystem:

Native .NET and C# Support

Semantic Kernel is built from the ground up for .NET. It follows .NET conventions, supports dependency injection, integrates with ASP.NET Core, and works seamlessly with the tooling and practices that .NET teams already use. There is no impedance mismatch or wrapper layer: it is idiomatic C# all the way through.

Production-Ready Architecture

Unlike many AI frameworks that are optimized for demos and prototypes, Semantic Kernel is designed for production workloads. It includes built-in support for structured error handling, retry policies, telemetry via OpenTelemetry, and configuration management. The framework has been battle-tested in Microsoft’s own products, including Microsoft 365 Copilot.

Azure OpenAI Integration

Semantic Kernel provides first-class integration with Azure OpenAI Service, including support for all available models, streaming responses, function calling, structured outputs, and content filtering. For enterprises using Azure, the integration is seamless and requires minimal configuration.

Enterprise Security Patterns

The framework supports managed identities, Azure Key Vault integration, and all Azure security best practices out of the box. Agent permissions can be controlled at the plugin level, ensuring that each agent only has access to the tools and data it needs.

Architecture Patterns with Semantic Kernel

Semantic Kernel supports a range of architecture patterns, from simple to sophisticated. Here are the three most common patterns we implement at Cloudkasten.

Pattern 1: Single Agent with Plugins

The simplest and most common pattern is a single AI agent equipped with a set of plugins that provide specific capabilities.

How it works:

• A single agent receives user requests or automated triggers
• The agent uses its LLM to understand the request and plan the response
• It calls registered plugins (tools, functions, APIs) as needed
• Results are synthesized and returned to the user or calling system

Best for: Focused automation tasks, customer service agents, internal assistants, document processing workflows.

Example: An invoice processing agent with plugins for document extraction (Azure AI Document Intelligence), ERP access (custom API plugin), validation rules (native function plugin), and notification (email plugin).

Pattern 2: Multi-Agent Collaboration

For complex workflows that span multiple domains, a multi-agent pattern distributes responsibilities across specialized agents that collaborate to achieve a shared goal.

How it works:

• A supervisor agent receives the initial request and determines which specialized agents to involve
• Specialized agents (e.g., research agent, analysis agent, writing agent) each handle their domain
• Agents communicate through a shared conversation or message-passing protocol
• The supervisor agent coordinates the workflow and assembles the final output

Best for: Complex research and reporting, multi-department workflows, scenarios requiring different expertise domains.

Example: A due diligence agent system where a data collection agent gathers financial information, a compliance agent checks regulatory requirements, a risk agent evaluates potential issues, and a report agent compiles the findings into a structured deliverable.

Pattern 3: RAG Pipeline with Agent Orchestration

Retrieval-augmented generation (RAG) is one of the most impactful patterns for enterprise AI. Semantic Kernel provides all the building blocks to implement sophisticated RAG pipelines.

How it works:

• Documents are ingested, chunked, and embedded into a vector store (e.g., Azure AI Search)
• When a query arrives, the agent generates search queries and retrieves relevant document chunks
• The agent synthesizes the retrieved information with its reasoning capabilities to produce grounded, accurate answers
• Source citations are included for traceability and verification

Best for: Knowledge management, customer support, regulatory compliance, technical documentation.

Example: An enterprise knowledge agent that searches across SharePoint, Confluence, and internal wikis to answer employee questions with sourced, up-to-date information. See our article on AI agents for process optimization for more practical examples.

Semantic Kernel vs. Alternatives

Enterprises evaluating AI agent frameworks often compare Semantic Kernel with other popular options. Here is an objective comparison:

When to Choose Semantic Kernel

• Your team works primarily in C# and .NET
• You are invested in the Microsoft and Azure ecosystem
• Enterprise security, compliance, and observability are requirements
• You need tight integration with Azure OpenAI Service
• You want a framework backed by Microsoft’s long-term commitment

When to Consider Alternatives

• Your team is Python-first with no .NET experience
• You need rapid prototyping with maximum library ecosystem
• Your AI infrastructure is not on Azure
• You are building research-oriented multi-agent systems (AutoGen)

Best Practices for Building with Semantic Kernel

Based on our production experience at Cloudkasten, here are key best practices:

1. Design plugins as clean, focused interfaces. Each plugin should represent a single capability domain. This makes plugins reusable across agents and easier to test.

2. Use dependency injection throughout. Semantic Kernel integrates with Microsoft.Extensions.DependencyInjection. Leverage this for clean architecture, testability, and configuration management.

3. Implement comprehensive logging and telemetry. Use Semantic Kernel’s built-in filter pipeline to log all LLM interactions, tool calls, and agent decisions. This data is invaluable for debugging and optimization.

4. Start simple and add complexity gradually. Begin with a single agent and a few plugins. Add planning, multi-agent patterns, and memory only when the use case genuinely requires them.

5. Test agents systematically. Build evaluation datasets for your agents and run automated quality checks. Test not just happy paths, but edge cases, error scenarios, and adversarial inputs.

Getting Started

Semantic Kernel and .NET provide a powerful, enterprise-ready foundation for building agentic AI systems. Combined with Azure OpenAI Service and proven architecture patterns, they enable development teams to deliver production-grade AI agents that integrate seamlessly with existing enterprise infrastructure.

Ready to build AI agents with Semantic Kernel? Contact our team to discuss your project and learn how we can help you get from concept to production.