Introduction to Semantic Kernel: The .NET Developer's Guide Building AI Agents

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1. Introduction: The Dawn of the AI-Powered .NET Application

Artificial intelligence is no longer a futuristic ideal reserved for research labs and Silicon Valley giants. It’s becoming the backbone of modern applications across industries. For .NET architects and developers, this shift signals a transformative opportunity to enhance software with intelligence that adapts, reasons, and learns. As business requirements grow more dynamic and expectations around user experience rise, AI isn’t just a differentiator. It’s rapidly becoming a core requirement.

1.1. The Paradigm Shift: Moving from Traditional Software to Intelligent Applications

Traditionally, software has been deterministic. You write rules, and the program executes them. These systems are reliable but limited; they can’t flexibly respond to ambiguous input or interpret intent beyond explicit instructions. The rise of large language models (LLMs) like GPT-4 and similar technologies marks a turning point. Instead of relying solely on rules, modern software can interpret, generate, and reason about text, code, and sometimes even images.

This evolution doesn’t mean replacing every deterministic algorithm with AI. Instead, it’s about infusing intelligence where it matters—understanding natural language, extracting meaning from documents, automating conversations, and orchestrating complex workflows based on human-like reasoning.

1.2. Why AI Orchestration is the Next Frontier for .NET Architects

You might be asking, “Can’t I just call an LLM API and get answers?” Technically, yes. But enterprise-grade applications require far more. They demand contextual awareness, composability, modularity, and robust integration with data and services. Making raw calls to a language model is like having a super-intelligent assistant who forgets everything between conversations and needs constant handholding.

Orchestration frameworks allow you to manage this intelligence—connecting LLMs to business logic, APIs, memory stores, and plugins—while maintaining the best practices you already follow in .NET development. AI orchestration bridges the gap between the potential of LLMs and the real-world needs of production software.

1.3. Introducing Semantic Kernel: Your Bridge to Large Language Models (LLMs)

Semantic Kernel, an open-source project from Microsoft, aims to make AI orchestration accessible, reliable, and powerful for .NET developers. It’s more than a wrapper for LLM APIs. Semantic Kernel gives you tools to:

Compose AI-driven workflows
Integrate semantic and native functions (both prompts and C# code)
Store and retrieve memory/context for stateful AI
Connect LLMs with real-world data and services

If you’re familiar with .NET patterns—dependency injection, plugin architectures, composable services—Semantic Kernel will feel like home, but with new capabilities that unlock generative AI.

1.4. What to Expect: A Practical Journey from Core Concepts to Enterprise-Ready AI Agents

This guide will walk you through everything you need to know as a .NET architect or senior developer who wants to build intelligent applications using Semantic Kernel. Whether your goal is a smart chatbot, a contextual document search, or an automated agent that can reason and act, you’ll find:

A clear explanation of LLMs from an architect’s perspective
How Semantic Kernel compares to other frameworks like LangChain
Step-by-step environment setup and code examples using the latest .NET features
A deep dive into Semantic Kernel’s core components: kernel, plugins, prompt engineering, and memory
Best practices, real-world scenarios, and practical tips for building robust AI agents

Are you ready to build the next generation of .NET applications? Let’s begin by understanding the technology at the heart of this new paradigm: large language models.

2. Understanding the Foundations: LLMs and the Rise of Semantic Kernel

2.1. A .NET Architect’s Primer on Large Language Models (LLMs)

2.1.1. What are LLMs and How Do They Work? (Conceptual Overview)

At their core, large language models (LLMs) like OpenAI’s GPT-4, Microsoft’s Phi-3, and Meta’s Llama are deep learning models trained on vast amounts of text data. They predict the next word in a sequence, given the previous words. But thanks to scale and architecture (transformers), they can generate coherent text, summarize documents, translate languages, answer questions, and even write code.

For a .NET developer, think of an LLM as a supercharged autocomplete on steroids. You provide input (the prompt), and the model returns output (the completion), whether it’s a sentence, an answer, or even a structured plan.

LLMs are not databases. They don’t “know” things in the way a SQL server does. Their knowledge is implicit, encoded in billions of parameters from training data. They excel at language understanding, pattern recognition, and analogical reasoning—but can’t guarantee factual accuracy or recall specific items outside their training window.

2.1.2. Key Terminology for Architects: Tokens, Embeddings, and Prompts

To use LLMs effectively, a few concepts are critical:

Tokens: These are the chunks of text (words, subwords, or characters) the model processes. For example, “.NET” may be one or more tokens. Cost and context length are often measured in tokens.
Embeddings: These are numerical representations of text. You can think of them as a way to compare the meaning of different pieces of text for similarity searches, clustering, or semantic retrieval.
Prompts: The input you send to the LLM. Crafting prompts is both an art and a science. The structure, clarity, and specificity of prompts heavily influence output quality.

Understanding these terms will help you make informed choices as you design intelligent .NET applications.

2.1.3. The Power and Limitations of LLMs in Enterprise Scenarios

LLMs offer a lot of promise: rapid prototyping, natural language interfaces, summarization, code generation, and more. But they have limitations:

Context size: LLMs have a maximum number of tokens they can process at once. Long documents may need to be chunked.
Factuality: LLMs can “hallucinate,” generating plausible-sounding but incorrect information.
Determinism: LLM outputs can vary based on randomness and prompt wording.
Latency and cost: LLM inference, especially with large models, may be slower and more expensive than traditional code execution.

Balancing these strengths and weaknesses is a key challenge in architecting real-world systems.

2.2. The Need for an Orchestration Framework: Why Raw LLM Calls Aren’t Enough

Calling an LLM directly from your .NET application can deliver a quick proof of concept, but scaling to production requires more:

State management: LLMs don’t remember previous interactions unless you provide them with context.
Tool use: LLMs can’t call APIs, access databases, or trigger workflows by themselves.
Security and compliance: Enterprise applications must handle data securely, log activity, and meet regulatory requirements.
Composability: Real solutions often require chaining multiple steps, integrating with external data, and combining native code with AI.

This is where orchestration frameworks like Semantic Kernel come in—wrapping raw LLM calls with structured components, context, plugins, and extensibility.

2.3. Semantic Kernel vs. The Competition: A .NET Perspective on LangChain

2.3.1. Core Philosophies and Architectural Differences

LangChain (popular in the Python ecosystem) and Semantic Kernel (for .NET and Python) both aim to orchestrate LLMs, memory, and tool usage. However, their core philosophies diverge:

LangChain: Emphasizes flexible chains, agents, and integrations. Designed for Python-first data science and rapid prototyping.
Semantic Kernel: Built natively for .NET (with a Python sibling). Focuses on composability, dependency injection, and familiar .NET patterns. Prioritizes strong typing, modularity, and integration with enterprise infrastructure (like Azure).

In short, Semantic Kernel is engineered for .NET professionals who want to blend AI with existing code, services, and enterprise patterns.

2.3.2. Why Semantic Kernel is a Natural Fit for the .NET Ecosystem

.NET architects value stability, maintainability, and the power of C#. Semantic Kernel fits right in:

First-class C# support: Write plugins and native functions in C# using the latest language features (including async/await, records, pattern matching, and more).
NuGet ecosystem: Easy installation, version management, and community support.
Integration with Microsoft services: Out-of-the-box connectors for Azure OpenAI, Azure Cognitive Search, and more.
Strong typing and dependency injection: Leverage established patterns for testing, scaling, and maintaining codebases.

If you’re building enterprise-grade software on .NET, Semantic Kernel offers an idiomatic and robust entry point into AI orchestration.

2.4. Setting Up Your Development Environment

Let’s get practical. To follow along, you’ll need:

2.4.1. Prerequisites: .NET 8, an IDE, and Your First “Hello, AI!” Project

Ensure you have the latest .NET 8 SDK installed. Any modern IDE works—Visual Studio 2022+, JetBrains Rider, or Visual Studio Code. Create a new console application:

dotnet new console -n HelloSemanticKernel cd HelloSemanticKernel

2.4.2. Installing the Semantic Kernel SDK via NuGet

Install the Semantic Kernel NuGet package. At the time of writing, use:

dotnet add package Microsoft.SemanticKernel

Check for the latest version on NuGet as Semantic Kernel evolves rapidly.

2.4.3. Configuring Your First Kernel with an LLM Connector (e.g., OpenAI, Azure OpenAI)

To use Semantic Kernel, you need to connect it to an LLM. Here’s a minimal example using OpenAI’s GPT-4:

using Microsoft.SemanticKernel; using Microsoft.SemanticKernel.Connectors.OpenAI; var builder = Kernel.CreateBuilder(); builder.AddOpenAIChatCompletion( modelId: "gpt-4", apiKey: "<your-openai-api-key>" ); var kernel = builder.Build(); var result = await kernel.InvokePromptAsync("What is Semantic Kernel?"); Console.WriteLine(result);

For Azure OpenAI, use AddAzureOpenAIChatCompletion instead, supplying the endpoint and deployment details. Keep your API keys secure—use user secrets or environment variables in real projects.

Now that you have a kernel connected to an LLM, you’re ready to explore Semantic Kernel’s architecture in depth.

3. The Core Components of Semantic Kernel: A Deep Dive

Understanding the pieces of Semantic Kernel is key to building powerful, maintainable, and intelligent applications. Let’s examine the main components: the kernel, plugins, prompt engineering, and memory.

3.1. The Kernel: The Heart of Your AI Application

3.1.1. Understanding the Kernel Class and its Role

The Kernel class is the orchestrator of your AI-powered .NET app. It manages the lifecycle of all your AI functions, plugins, connectors, and memory. Think of it as the AI engine—receiving user requests, delegating work to plugins, passing context between functions, and interacting with LLMs.

3.1.2. The KernelBuilder: Configuring Services and Dependencies

Much like HostBuilder in ASP.NET Core, KernelBuilder helps you configure dependencies and register services. You can add connectors for different LLMs, configure memory stores, and register plugins. This pattern aligns well with modern .NET practices and makes your application easier to test and maintain.

Example: Configuring Kernel with Multiple Services

using Microsoft.SemanticKernel; using Microsoft.SemanticKernel.Connectors.OpenAI; var builder = Kernel.CreateBuilder(); // Register OpenAI for text completion builder.AddOpenAIChatCompletion( modelId: "gpt-4", apiKey: "<api-key>" ); // Register memory with Azure Cognitive Search builder.AddAzureCognitiveSearchMemory( endpoint: "<azure-endpoint>", apiKey: "<azure-key>" ); // Register custom plugins builder.Plugins.AddFromType<MyNativePlugin>(); var kernel = builder.Build();

This modular approach allows you to swap components or add new ones as your solution evolves.

3.2. Plugins: The Building Blocks of Functionality

3.2.1. What are Plugins? A Blend of Native C# and Semantic Functions

Plugins in Semantic Kernel encapsulate functionality that can be invoked by the kernel. They come in two flavors:

Native functions: Pure C# code, ideal for deterministic logic, API calls, or data processing.
Semantic functions: Prompt-based, executed via LLMs, useful for natural language understanding, summarization, and generative tasks.

Plugins can expose one or more functions, and can be invoked directly or composed into workflows.

3.2.2. Creating Your First Native Function in C#

Here’s how you might define a simple native plugin in C#:

using Microsoft.SemanticKernel.SkillDefinition; public class MathPlugin { [KernelFunction("add")] public Task<int> AddAsync(int a, int b) { return Task.FromResult(a + b); } } // Register plugin builder.Plugins.AddFromType<MathPlugin>();

This makes your C# function callable by other plugins, prompts, or LLM-driven workflows.

3.2.3. Crafting Your First Semantic Function with Prompts

Semantic functions are defined with prompt templates. Here’s an example for summarizing text:

Semantic functions can be registered as part of a plugin for reuse and composition.

3.2.4. Best Practices for Designing and Organizing Plugins

Separation of concerns: Group related functions into plugins for clarity and maintainability.
Reusability: Keep plugins stateless where possible. Store state in memory/context passed between invocations.
Strong typing: Use well-defined input/output types for native functions.
Error handling: Anticipate and gracefully handle failures, especially for external API calls.

Organize plugins much like you would controllers or services in an ASP.NET project.

3.3. Prompts and Prompt Engineering for .NET Developers

3.3.1. The Art of the Prompt: From Simple Instructions to Complex Templates

Prompt engineering is the craft of designing inputs that reliably elicit high-quality outputs from LLMs. A well-structured prompt clarifies intent and reduces ambiguity.

Simple prompt example:

"Translate the following text to French: {{$input}}"

Complex template with variables:

"You are an expert .NET software architect. Summarize the following requirements, and suggest a suitable design pattern. Requirements: {{$requirements}}"

Testing and refining prompts is iterative. Small changes can have large impacts on output quality.

3.3.2. Using PromptTemplate and PromptTemplateFactory

Semantic Kernel provides APIs for building reusable prompt templates.

using Microsoft.SemanticKernel.TemplateEngine; var templateFactory = new PromptTemplateFactory(); var template = templateFactory.Create("Rewrite the following as a bullet list: {{$input}}"); var kernelFunction = kernel.CreateSemanticFunction(template);

You can parameterize templates for dynamic inputs and maintain a library of prompts for your application.

3.3.3. Advanced Prompting Techniques: Few-shot Learning and Chain of Thought

Few-shot learning: Include examples in your prompt to guide the LLM.

"Convert date formats. Example: '2025-06-20' -> 'June 20, 2025'. Now, convert: '{{$input}}'"

Chain of thought: Encourage the model to “think aloud” by requesting step-by-step reasoning.

"Solve the following math problem step by step: {{$input}}"

Use these techniques to improve reliability for complex tasks.

3.4. Memory: Giving Your AI a Past and a Context

3.4.1. The Importance of Memory in Conversational AI

Stateless LLMs are like goldfish—they forget each interaction. For many applications, especially chatbots and agents, you need to maintain conversation history, user preferences, or task progress. Memory makes your AI feel intelligent, personal, and context-aware.

3.4.2. Volatile Memory: Quick, In-Process Context

Semantic Kernel supports volatile, in-memory storage for session-level context.

Example: Adding and retrieving memory within a session

var memory = kernel.Memory; // Save data await memory.SaveInformationAsync( collection: "chatHistory", text: "Hello, how can I help you?", id: "message1" ); // Retrieve data var message = await memory.GetAsync("chatHistory", "message1"); Console.WriteLine(message?.Metadata.Text);

Volatile memory is fast, but not persistent. Use it for short-lived state.

3.4.3. Semantic Memory and Vector Databases (e.g., Azure AI Search, Pinecone, Chroma)

For richer, persistent memory, connect Semantic Kernel to a vector database. Text is stored as embeddings, enabling similarity search and semantic retrieval.

Configuring Azure Cognitive Search memory:

builder.AddAzureCognitiveSearchMemory( endpoint: "<azure-endpoint>", apiKey: "<azure-key>" );

Usage pattern:

Store documents, conversation history, or knowledge snippets as embeddings.
At runtime, retrieve the most relevant items based on semantic similarity to the current context.

This unlocks powerful capabilities like RAG (retrieval-augmented generation), where your LLM can “recall” enterprise knowledge.

3.4.4. Practical Example: Building a Chatbot with Conversational History

Let’s combine the concepts:

// Assume kernel and memory are already configured string userInput = "How do I connect to Azure from .NET?"; await memory.SaveInformationAsync("chatHistory", userInput, Guid.NewGuid().ToString()); string conversation = string.Join("\n", await memory.SearchAsync("chatHistory", "", limit: 10)); // Build prompt with context string prompt = $"The following is a conversation between a user and an assistant.\n{conversation}\nUser: {userInput}\nAssistant:"; // Create semantic function var chatFunction = kernel.CreateSemanticFunction(prompt); // Invoke LLM var response = await kernel.InvokeAsync(chatFunction); Console.WriteLine(response); // Save assistant’s response await memory.SaveInformationAsync("chatHistory", response.ToString(), Guid.NewGuid().ToString());

This pattern forms the

backbone of context-aware chatbots, assistants, and intelligent agents.

4. Connectors: Extending the Reach of Your AI Agent

Enterprise applications rarely operate in isolation. The true power of Semantic Kernel emerges when it acts as a hub, seamlessly integrating with external data sources, services, and infrastructure. This extensibility is enabled by connectors. Understanding and leveraging connectors allows your AI agents not only to “think” but also to act within the broader technology landscape.

4.1. The Role of Connectors in an Extensible Architecture

Connectors in Semantic Kernel are modular interfaces that enable your kernel to interact with the outside world. At a high level, connectors abstract communication with:

Language models (LLM connectors)
Memory stores (memory connectors)
Data sources (SQL, REST APIs, or any service you need to orchestrate)

For .NET architects, connectors represent clear separation of concerns—each connector encapsulates integration logic for a specific external resource, making your system more maintainable and adaptable. Whether you need to switch LLM providers, swap out a memory store, or add a custom business system, connectors make this evolution straightforward.

In essence, connectors enable your AI agent to become truly actionable and context-aware, with the ability to see, remember, and interact with the resources you define.

4.2. LLM Connectors: Beyond OpenAI

4.2.1. Integrating with Azure OpenAI for Enterprise-Grade Security and Scalability

While OpenAI’s hosted APIs are popular for experimentation and smaller-scale deployments, many organizations require greater control over data residency, compliance, throughput, and security. This is where Azure OpenAI shines.

Azure OpenAI offers:

Enterprise security (AAD integration, VNETs, private endpoints)
Scalability (custom model deployments, throughput guarantees)
Compliance with regional and industry regulations

Configuring Semantic Kernel with Azure OpenAI:

builder.AddAzureOpenAIChatCompletion( deploymentName: "my-gpt4-deployment", endpoint: "https://<your-resource>.openai.azure.com/", apiKey: "<your-azure-api-key>" ); var kernel = builder.Build();

The main difference: deploymentName is your model deployment in Azure, not just a model ID. For organizations already leveraging Azure, this integration provides peace of mind and seamless management.

4.2.2. Exploring Other LLM Providers (e.g., Hugging Face)

Semantic Kernel is LLM-agnostic by design. You can connect to any provider—local or cloud—using available connectors or by writing your own. For example, integrating with Hugging Face’s hosted models is a viable alternative for teams who prefer open models, require domain-specific fine-tuning, or wish to avoid vendor lock-in.

Using Hugging Face with Semantic Kernel:

While direct support may not be built-in, the extensibility of Semantic Kernel allows you to wrap any RESTful LLM endpoint as a connector. This flexibility empowers you to leverage open-source models, self-hosted LLMs, or niche providers as your needs evolve.

4.3. Memory Connectors: Persistent and Scalable Memory Solutions

Your AI agent’s value increases dramatically when it has access to organizational knowledge and history. Memory connectors enable persistent, scalable storage and retrieval—powering advanced scenarios like Retrieval Augmented Generation (RAG), contextual chat, and semantic search.

4.3.1. Connecting to Azure AI Search for a Robust RAG Implementation

Azure AI Search (previously Azure Cognitive Search) is a fully managed search-as-a-service, providing fast, scalable, and enterprise-ready vector search capabilities. When integrated with Semantic Kernel, it enables your agent to recall relevant documents, past interactions, and knowledge snippets efficiently.

Example: Registering Azure AI Search Memory

builder.AddAzureCognitiveSearchMemory( endpoint: "https://<your-search-service>.search.windows.net", apiKey: "<your-search-api-key>", indexName: "support-tickets" );

With this setup, your AI agent can persist and retrieve embeddings (semantic representations of text), supporting sophisticated RAG pipelines and contextual reasoning over vast data sets.

4.3.2. Integrating with Other Vector Stores

Semantic Kernel’s abstraction makes it easy to use alternative vector databases:

Pinecone
Chroma
Qdrant
Redis with vector extensions
Any custom solution exposing a REST API

You simply implement or use the appropriate memory connector, registering it with your kernel. This means your architecture can evolve with minimal code changes, future-proofing your AI systems as technology advances.

4.4. Building Custom Connectors: Tailoring Semantic Kernel to Your Needs

Not every integration comes out-of-the-box. Sometimes, your agent needs to interact with proprietary systems, legacy APIs, or specialized databases. Semantic Kernel’s connector model is fully extensible. You can build custom connectors by implementing the required interfaces.

High-level pattern:

Implement the connector interface (e.g., IMemoryStore, IChatCompletionService).
Register your connector with the kernel via the builder.
Use the connector as part of your agent’s orchestration flow.

This approach maintains separation of concerns and adheres to SOLID principles—letting your AI orchestration logic evolve independently from integration details.

5. Building Your First AI Agent: A Practical, Step-by-Step Guide

It’s time to put these concepts into practice. Let’s build a fully functional, production-minded AI agent using Semantic Kernel. We’ll walk through a concrete use case, craft plugins, wire up connectors, and orchestrate everything with a planner.

5.1. Defining the Use Case: A “Smart Support Ticket” System

Support operations are an ideal proving ground for AI agents. Incoming tickets contain natural language—sometimes vague, sometimes lengthy—and require prompt, consistent, and contextually relevant responses.

5.1.1. The Goal: Automatically Categorize, Prioritize, and Suggest Solutions for Support Tickets

Our AI agent will:

Analyze the severity of a new support ticket
Categorize it by department (e.g., IT, HR, Facilities)
Retrieve and suggest solutions from similar past tickets

This automation reduces manual triage, improves response times, and ensures knowledge is reused. It also demonstrates how Semantic Kernel blends native C#, semantic prompts, memory, and connectors.

5.2. Step 1: Setting up the Kernel and Plugins

5.2.1. A Plugin to Analyze Ticket Severity (Semantic Function)

We’ll create a semantic function that prompts the LLM to assess severity.

var analyzeSeverity = kernel.CreateSemanticFunction( @"You are a customer support agent. Assess the severity of the following support ticket on a scale from 1 (trivial) to 5 (critical). Ticket: {{$input}} Respond with only the number." );

This approach leverages LLM strengths: nuanced understanding of language and context.

5.2.2. A Plugin to Categorize by Department (Semantic Function)

Categorization by department is another classic LLM task. Here’s the function:

var categorizeDepartment = kernel.CreateSemanticFunction( @"Assign the following support ticket to the most relevant department: IT, HR, Facilities, Finance, or Other. Ticket: {{$input}} Respond with only the department name." );

Both plugins could be grouped in a “SupportTicketSemanticPlugin” for clarity and reusability.

5.2.3. A Plugin to Look Up Similar Historical Tickets (Native C# with Memory)

While LLMs excel at language, finding similar tickets from memory is best handled natively—leveraging semantic memory for vector search.

public class TicketMemoryPlugin { private readonly ISemanticTextMemory _memory; public TicketMemoryPlugin(ISemanticTextMemory memory) => _memory = memory; [KernelFunction] public async Task<string> FindSimilarAsync(string input) { var results = await _memory.SearchAsync("support-tickets", input, limit: 3, minRelevanceScore: 0.7); return string.Join("\n", results.Select(r => r.Metadata.Text)); } }

builder.Plugins.AddFromType<TicketMemoryPlugin>();

5.3. Step 2: Orchestrating the Flow with a Planner

5.3.1. Introduction to Planners: The “Brain” of the Agent

A planner in Semantic Kernel orchestrates multiple functions—semantic and native—into a workflow. Think of the planner as the agent’s executive: it sequences actions, manages dependencies, and ensures logical flow from input to output.

Planners are especially useful when your AI agent’s behavior involves multiple steps, data dependencies, or dynamic branching.

5.3.2. Using the SequentialPlanner for a Step-by-Step Workflow

For our support ticket system, a sequential approach works well:

Assess severity
Categorize by department
Retrieve similar historical tickets

Semantic Kernel provides a SequentialPlanner to chain these steps.

using Microsoft.SemanticKernel.Planning; var planner = new SequentialPlanner(kernel); var plan = await planner.CreatePlanAsync( @"Given a new support ticket, perform the following: 1. Analyze the ticket’s severity. 2. Categorize it by department. 3. Look up similar historical tickets. Output the results as a JSON object." );

The planner inspects your registered plugins and functions, building an executable workflow tailored to your agent’s capabilities.

5.4. Step 3: Executing the Plan and Handling the Output

5.4.1. Invoking the Planner and Retrieving the Result

With the plan in place, you execute it with a new support ticket as input.

string ticketText = "My laptop screen is flickering, and I need it fixed urgently for a client presentation tomorrow."; var result = await plan.InvokeAsync(kernel, ticketText); Console.WriteLine(result.ToString());

The output—ideally a structured JSON object—includes severity, department, and suggested solutions.

5.4.2. Integrating the Output with a Hypothetical Ticketing System API

In a real-world system, your agent’s output feeds into your support ticketing backend. Here’s a conceptual example using an HTTP client:

using System.Net.Http.Json; public class TicketingSystemClient { private readonly HttpClient _httpClient; public TicketingSystemClient(HttpClient httpClient) => _httpClient = httpClient; public async Task PostCategorizedTicketAsync(string ticketId, object analysisResult) { await _httpClient.PostAsJsonAsync($"/api/tickets/{ticketId}/analysis", analysisResult); } } // In your workflow: var ticketingClient = new TicketingSystemClient(httpClient); await ticketingClient.PostCategorizedTicketAsync("12345", result);

This decouples your AI logic from backend infrastructure and enables clean integration patterns.

5.5. Code Walkthrough and Explanation

Let’s bring the pieces together for a clear, end-to-end understanding.

using Microsoft.SemanticKernel; using Microsoft.SemanticKernel.Connectors.OpenAI; using Microsoft.SemanticKernel.Planning; using Microsoft.SemanticKernel.Memory; using Microsoft.SemanticKernel.SkillDefinition; using System.Net.Http.Json; // 1. Set up kernel, memory, and plugins var builder = Kernel.CreateBuilder(); builder.AddOpenAIChatCompletion("gpt-4", "<api-key>"); builder.AddAzureCognitiveSearchMemory("https://<search>.search.windows.net", "<key>", "support-tickets"); // Register semantic plugins var kernel = builder.Build(); var analyzeSeverity = kernel.CreateSemanticFunction( @"You are a customer support agent. Assess the severity of the following support ticket on a scale from 1 (trivial) to 5 (critical). Ticket: {{$input}} Respond with only the number." ); var categorizeDepartment = kernel.CreateSemanticFunction( @"Assign the following support ticket to the most relevant department: IT, HR, Facilities, Finance, or Other. Ticket: {{$input}} Respond with only the department name." ); public class TicketMemoryPlugin { private readonly ISemanticTextMemory _memory; public TicketMemoryPlugin(ISemanticTextMemory memory) => _memory = memory; [KernelFunction] public async Task<string> FindSimilarAsync(string input) { var results = await _memory.SearchAsync("support-tickets", input, limit: 3, minRelevanceScore: 0.7); return string.Join("\n", results.Select(r => r.Metadata.Text)); } } builder.Plugins.AddFromType<TicketMemoryPlugin>(); // 2. Define planner and plan var planner = new SequentialPlanner(kernel); var plan = await planner.CreatePlanAsync( @"Given a new support ticket, perform the following: 1. Analyze the ticket’s severity. 2. Categorize it by department. 3. Look up similar historical tickets. Output the results as a JSON object." ); // 3. Run the agent string ticketText = "My laptop screen is flickering, and I need it fixed urgently for a client presentation tomorrow."; var result = await plan.InvokeAsync(kernel, ticketText); Console.WriteLine(result.ToString()); // 4. Optionally, send result to ticketing API var httpClient = new HttpClient { BaseAddress = new Uri("https://yourticketingsystem/api/") }; var ticketingClient = new TicketingSystemClient(httpClient); await ticketingClient.PostCategorizedTicketAsync("12345", result);

Explanation and Takeaways:

Separation of concerns: Semantic functions handle natural language understanding, native C# manages structured tasks and API calls.
Modularity: Each capability is encapsulated in a plugin, supporting reuse and evolution.
Extensibility: Connectors allow you to swap LLMs or memory stores as requirements change.
Orchestration: The planner composes these functions into a reliable, repeatable workflow—abstracting complexity.

This pattern is adaptable: swap in more departments, connect new memory sources, add escalation logic, or build user-facing interfaces. The architecture stays robust and comprehensible, scaling from proof of concept to enterprise deployment.

6. Advanced Concepts for the .NET Architect

At this stage, you’ve mastered the fundamentals of Semantic Kernel. But to build AI agents that truly operate at scale, deliver business-critical value, and fit seamlessly into your architecture, you’ll want to explore the advanced capabilities at your disposal. Let’s unpack the next level of patterns, scalability, and operational excellence.

6.1. Planners in Depth: Beyond Simple Sequences

6.1.1. The Evolution of Planners: From ActionPlanner to StepwisePlanner

The basic SequentialPlanner serves well for linear workflows. However, real-world applications often demand more adaptive, dynamic behavior. Semantic Kernel introduces advanced planners that provide these capabilities.

ActionPlanner: Interprets natural language goals and generates a sequence of plugin calls to fulfill them. It uses LLM reasoning to determine which functions to invoke and in what order.
StepwisePlanner: Takes adaptivity further. This planner runs in a loop, allowing the agent to adjust its strategy based on intermediate outputs. It’s akin to a human iteratively working toward a goal, reevaluating each step as new information emerges.

These planners empower your agent to tackle more ambiguous tasks, handle branching logic, and even recover from errors during multi-step operations.

6.1.2. The Power of Function Calling and Auto-invocation

One of the most transformative advancements is the introduction of function calling. This enables LLMs to recognize when specific structured actions are needed and automatically invoke plugins or functions, passing the right parameters.

For .NET architects, this is a leap towards autonomous agents: LLMs are no longer just text generators, but can act on structured data, call APIs, and respond to system events—all orchestrated by the kernel.

With auto-invocation, the kernel monitors output from the LLM, detects embedded function calls, and executes the corresponding native or semantic functions—creating a fluid loop of reasoning and action.

6.1.3. Building More Complex, Goal-Oriented Agents

Suppose you want your agent to resolve a support case, escalate when necessary, retrieve data, and update the ticketing system—all based on an open-ended prompt. Here’s how you’d use a planner with function calling:

var plan = await actionPlanner.CreatePlanAsync( "For the following support request, diagnose the problem, suggest next steps, and escalate if unresolved. Ticket: {{$ticketDescription}}" ); var result = await plan.InvokeAsync(kernel, new KernelArguments { ["ticketDescription"] = ticket });

The planner, powered by the LLM, will automatically select functions—be they semantic or native—and chain them as needed, reacting dynamically to each output.

6.2. Architectural Patterns for Semantic Kernel Applications

6.2.1. The “Agent” Pattern: Encapsulating AI Logic

The agent pattern encapsulates your orchestration logic, plugins, and connectors behind a cohesive interface. This makes your AI component easily reusable and testable.

Pattern example:

public interface ISupportAgent { Task<TicketAnalysisResult> AnalyzeTicketAsync(string ticketText); } public class SupportAgent : ISupportAgent { private readonly IKernel _kernel; private readonly IPlanner _planner; // Constructor omitted for brevity public async Task<TicketAnalysisResult> AnalyzeTicketAsync(string ticketText) { var result = await _planner.InvokeAsync(_kernel, ticketText); // Map output to strong-typed result return ParseResult(result); } }

This aligns with .NET’s focus on strong typing and interface-driven development.

6.2.2. The “Chain of Responsibility” Pattern with Plugins

Often, multiple plugins can handle a request, and you may want to try them in sequence until one succeeds. This pattern, well-known in .NET circles, works naturally with Semantic Kernel plugins.

Usage example:

foreach (var plugin in plugins) { var response = await plugin.TryHandleAsync(request); if (response.Success) return response; }

This lets you layer fallback strategies, combine traditional code with LLM-driven logic, and make your agents more resilient.

6.2.3. Designing for Testability: Mocking and Unit Testing Semantic Kernel Components

Testability is essential for enterprise-grade AI solutions. Semantic Kernel supports dependency injection, enabling you to inject mock connectors and services.

Testing pattern:

Use interfaces (e.g., IMemoryStore, IChatCompletionService)
Register mocks or in-memory implementations during testing
Assert that planners and plugins behave as expected for given inputs

Example with a mocking library:

var mockMemory = new Mock<IMemoryStore>(); mockMemory.Setup(m => m.SearchAsync(...)).ReturnsAsync(...); // Inject mockMemory into your agent for controlled unit tests

By decoupling orchestration from external services, you maintain high coverage and robust, change-resilient code.

6.3. Scalability, Performance, and Cost Management

6.3.1. Caching Strategies for LLM Responses

LLM calls are expensive and sometimes slow. Implementing response caching can reduce both cost and latency.

Short-term cache: Store responses for recent prompts in-memory (e.g., using MemoryCache).
Persistent cache: For common queries, use distributed caches like Redis.

Pattern example:

var cacheKey = HashPrompt(prompt); if (_cache.TryGetValue(cacheKey, out var cachedResponse)) return cachedResponse; // Otherwise, call LLM and cache result var result = await kernel.InvokePromptAsync(prompt); _cache.Set(cacheKey, result, TimeSpan.FromHours(1));

6.3.2. Asynchronous Processing for Long-Running AI Tasks

Some AI workflows (like document summarization) can be time-consuming. Use asynchronous APIs throughout your Semantic Kernel code, and consider offloading to background processing systems (e.g., Azure Functions, Hangfire) for heavy workloads.

6.3.3. Token Management and Optimizing for Cost

LLM cost is often tied directly to token usage (input + output). Monitor and optimize:

Trim unnecessary context and verbose prompts
Chunk long documents before sending to the LLM
Aggregate requests when possible

Semantic Kernel lets you manage and inspect token usage, so you can track and optimize at every step.

6.4. Observability and Monitoring

6.4.1. Integrating with Application Insights for Telemetry

Operational visibility is crucial. Semantic Kernel plays well with .NET observability tooling. With Azure Application Insights, you can track:

LLM call durations and success rates
Planner execution steps
Memory and connector performance

Integrate with ILogger and diagnostic events for custom metrics.

6.4.2. Logging and Debugging Semantic Kernel Workflows

Use structured logging (ILogger<T>) throughout your plugins and planners:

_logger.LogInformation("Invoking AnalyzeTicket function with prompt: {Prompt}", prompt);

When debugging, capture intermediate states, planner decisions, and kernel context objects. For production, ensure that sensitive information is never logged, and use log levels appropriately.

7. Real-World Implementation: A “Corporate Knowledge Base” AI Agent

Let’s bring all these concepts together with a scenario increasingly relevant in today’s enterprises: an AI-powered knowledge base assistant.

7.1. The Vision: An AI Assistant that Answers Employee Questions Using Internal Documentation

Imagine a virtual assistant that fields employee questions—about benefits, IT issues, HR policies, or procedures—by searching your organization’s internal documentation, summarizing answers, and always providing sources. This isn’t just search. It’s semantic reasoning over your knowledge base.

7.2. Architecture Overview

7.2.1. Leveraging Retrieval-Augmented Generation (RAG)

RAG marries the strength of LLMs with precise, organization-specific retrieval:

Retrieve: Use vector search to find relevant documents/snippets based on user queries.
Augment: Feed these results into the LLM as context.
Generate: Ask the LLM to synthesize a natural-language answer, citing sources.

This pattern enables factually grounded, up-to-date responses—critical for business trust.

7.2.2. Components: ASP.NET Core API, Semantic Kernel, Azure OpenAI, and Azure AI Search

A modern .NET knowledge agent typically consists of:

ASP.NET Core Web API: Receives user queries and returns answers
Semantic Kernel orchestration: Manages retrieval, prompt construction, and LLM calls
Azure OpenAI: Provides enterprise LLM capabilities
Azure AI Search: Delivers scalable vector retrieval over corporate documents

7.3. Step-by-Step Implementation Guide

7.3.1. Ingesting and Indexing Corporate Documents into Azure AI Search

Begin by converting your internal documentation (PDFs, docs, wikis) into text and splitting into chunks. Each chunk is vectorized (converted to embeddings) and indexed in Azure AI Search.

Use Azure’s built-in skills or your own text extractors.
Index metadata (source, title, URL) alongside the text.

7.3.2. Building a “KnowledgeBase” Plugin with Semantic Memory

Implement a plugin that retrieves the most relevant document chunks for a given query:

public class KnowledgeBasePlugin { private readonly ISemanticTextMemory _memory; public KnowledgeBasePlugin(ISemanticTextMemory memory) => _memory = memory; [KernelFunction] public async Task<string> RetrieveRelevantSnippetsAsync(string question) { var results = await _memory.SearchAsync("knowledge-base", question, limit: 5, minRelevanceScore: 0.75); return string.Join("\n", results.Select(r => $"{r.Metadata.Text} [Source: {r.Metadata.ExternalSourceName}]")); } }

7.3.3. Creating an API Endpoint to Handle User Queries

Wire up your orchestration in a controller:

[ApiController] [Route("api/knowledge")] public class KnowledgeController : ControllerBase { private readonly IKernel _kernel; public KnowledgeController(IKernel kernel) => _kernel = kernel; [HttpPost("ask")] public async Task<IActionResult> Ask([FromBody] QuestionRequest request) { var snippets = await _kernel.InvokeFunctionAsync("KnowledgeBasePlugin", "RetrieveRelevantSnippetsAsync", request.Question); var answer = await _kernel.InvokePromptAsync(CreateKnowledgeBasePrompt(request.Question, snippets)); return Ok(new { answer = answer.ToString() }); } }

7.3.4. Crafting a Sophisticated Prompt to Synthesize Answers from Retrieved Documents

Prompts are the heart of RAG. You want to give the LLM enough context, but also instruct it to cite sources and admit uncertainty.

Example prompt template:

string CreateKnowledgeBasePrompt(string question, string context) => $@"You are an expert corporate assistant. Using only the following documentation snippets, answer the question below. If the answer is not found, say 'I don't know'. Documentation: {context} Question: {question} Answer (cite sources):";

7.3.5. Handling “I don’t know” Scenarios and Providing Source Links

Effective assistants admit gaps in knowledge. Make it explicit in your prompt, and in post-processing, highlight when the LLM says “I don’t know.”

You can also parse the output for [Source: ...] tags and render clickable links in your frontend.

7.4. Deployment and CI/CD Considerations for AI Applications

7.4.1. Managing Prompts and Configurations in a Production Environment

Prompts evolve. Store them in a configuration service (Azure App Configuration, Key Vault, or even database) rather than code, and support dynamic reloads. Version and test prompt changes before rollout.

7.4.2. A/B Testing Different Prompts and Models

Continuous improvement is key. Route a fraction of users to different prompts or model configurations. Track outcomes, user feedback, and usage metrics. Use this data to refine your knowledge agent, ensuring it gets smarter and more helpful over time.

8. The Future of Semantic Kernel and AI in the .NET Ecosystem

The AI landscape is evolving rapidly:

Multi-modal models (text, images, audio, code) are on the horizon. Semantic Kernel is architected to extend to these new modalities, allowing agents to process and generate across domains.
Autonomous agents are moving from research to practice. Expect planners to become more sophisticated, blending symbolic reasoning with LLM-powered decision-making.
Tighter integration with cloud-native and on-premises resources will further blur the line between AI agents and traditional software components.

8.2. The Growing Semantic Kernel Community and Resources

The Semantic Kernel community is expanding, with contributions from both Microsoft and open-source developers. You’ll find:

Regular releases and feature additions
A lively GitHub repository with issue tracking, discussions, and roadmap visibility
Community samples, recipes, and deployment guides
Integration projects for other languages (e.g., Python) and frameworks

Engage early. Contribute plugins, suggest features, and shape the evolution of .NET AI orchestration.

8.3. Final Thoughts: Empowering the Next Generation of .NET Applications

Semantic Kernel does more than enable AI in .NET. It provides a foundation for composable, testable, and maintainable intelligent systems that solve real business problems. The architects and developers who master these patterns will lead their organizations into the AI-powered future, delivering solutions that are not only smart, but also robust and trustworthy.

9. Appendix

9.1. Glossary of Key Terms

LLM (Large Language Model): AI models trained on large corpora to understand and generate natural language.
Prompt: Input sent to an LLM to elicit a specific response.
Embedding: A vector representation of text for semantic search and retrieval.
RAG (Retrieval-Augmented Generation): Pattern combining document retrieval with LLM synthesis.
Connector: Modular component enabling integration with external LLMs or memory stores.
Planner: Module that sequences function calls, often with LLM input, to achieve a goal.
Semantic Function: Function powered by an LLM prompt.
Native Function: Function written in C#, performing deterministic logic or integrations.

9.2. Useful Links and Resources

9.3. Full Code Examples

For complete, working code, visit the semantic-kernel GitHub repository or adapt the snippets provided in each section above to your solution.

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