Introduction
Microsoft’s Semantic Kernel is a powerful framework that enables developers to integrate large language models (LLMs) into their applications seamlessly. Whether you’re building chatbots, content generators, or intelligent automation tools, Semantic Kernel provides the building blocks to create sophisticated AI-powered agents.
In this post, we’ll explore how to build a simple yet effective Semantic Kernel agent in C# that can understand user requests, plan actions, and execute tasks autonomously.
What is Semantic Kernel?
Semantic Kernel is an open-source SDK that allows developers to:
- Integrate AI services like OpenAI GPT, Azure OpenAI, and other language models
- Create plugins that extend AI capabilities with custom functions
- Build AI agents that can plan and execute multi-step tasks
- Combine traditional programming with AI-powered natural language processing
Think of it as a bridge between your application logic and AI services, providing a structured way to build intelligent applications.
Why Do You Need an Agent?
Traditional AI integrations often involve simple request-response patterns. However, agents take this a step further by:
- Autonomous Decision Making: Agents can analyze user requests and determine the best course of action
- Multi-step Planning: They can break down complex tasks into smaller, manageable steps
- Tool Integration: Agents can use various tools and APIs to accomplish goals
- Context Awareness: They maintain conversation context and can reference previous interactions
Building a Simple Semantic Kernel Agent
Let’s create a basic agent that can help with file operations and web searches. Here’s a minimal working example:
Step 1: Install Required Packages
First, install the necessary NuGet packages:
dotnet add package Microsoft.SemanticKernel dotnet add package Microsoft.SemanticKernel.Plugins.Core
Step 2: Create the Agent
using Microsoft.SemanticKernel;
using Microsoft.SemanticKernel.ChatCompletion;
using Microsoft.SemanticKernel.Connectors.OpenAI;
using System.ComponentModel;
public class SimpleSemanticKernelAgent
{
private readonly Kernel _kernel;
private readonly IChatCompletionService _chatService;
private readonly ChatHistory _chatHistory;
public SimpleSemanticKernelAgent(string apiKey, string model = "gpt-3.5-turbo")
{
// Create kernel builder
var builder = Kernel.CreateBuilder();
// Add OpenAI chat completion service
builder.AddOpenAIChatCompletion(model, apiKey);
// Add plugins
builder.Plugins.AddFromType<FileOperationsPlugin>();
builder.Plugins.AddFromType<WebSearchPlugin>();
// Build kernel
_kernel = builder.Build();
// Get chat completion service
_chatService = _kernel.GetRequiredService<IChatCompletionService>();
// Initialize chat history
_chatHistory = new ChatHistory();
_chatHistory.AddSystemMessage(
"You are a helpful assistant that can perform file operations and web searches. " +
"When users ask for help, analyze their request and use the available tools to assist them.");
}
public async Task<string> ProcessUserRequestAsync(string userInput)
{
try
{
// Add user message to history
_chatHistory.AddUserMessage(userInput);
// Configure execution settings
var executionSettings = new OpenAIPromptExecutionSettings
{
ToolCallBehavior = ToolCallBehavior.AutoInvokeKernelFunctions,
MaxTokens = 1000,
Temperature = 0.7
};
// Get response from the agent
var response = await _chatService.GetChatMessageContentAsync(
_chatHistory,
executionSettings,
_kernel);
// Add assistant response to history
_chatHistory.AddAssistantMessage(response.Content ?? "");
return response.Content ?? "I'm sorry, I couldn't process your request.";
}
catch (Exception ex)
{
return $"An error occurred: {ex.Message}";
}
}
}
// Example plugin for file operations
public class FileOperationsPlugin
{
[KernelFunction, Description("Read content from a text file")]
public async Task<string> ReadFileAsync(
[Description("Path to the file to read")] string filePath)
{
try
{
if (!File.Exists(filePath))
return "File not found.";
return await File.ReadAllTextAsync(filePath);
}
catch (Exception ex)
{
return $"Error reading file: {ex.Message}";
}
}
[KernelFunction, Description("Write content to a text file")]
public async Task<string> WriteFileAsync(
[Description("Path to the file to write")] string filePath,
[Description("Content to write to the file")] string content)
{
try
{
await File.WriteAllTextAsync(filePath, content);
return "File written successfully.";
}
catch (Exception ex)
{
return $"Error writing file: {ex.Message}";
}
}
}
// Example plugin for web search (simplified)
public class WebSearchPlugin
{
[KernelFunction, Description("Search the web for information")]
public async Task<string> SearchWebAsync(
[Description("Search query")] string query)
{
// In a real implementation, you would integrate with a search API
// like Bing Search API, Google Custom Search, etc.
await Task.Delay(1000); // Simulate API call
return $"Search results for '{query}': [This is a simplified example. " +
"In a real implementation, you would return actual search results.]";;
}
}
Step 3: Using the Agent
class Program
{
static async Task Main(string[] args)
{
// Initialize the agent with your OpenAI API key
var agent = new SimpleSemanticKernelAgent("your-openai-api-key-here");
Console.WriteLine("Semantic Kernel Agent initialized. Type 'exit' to quit.");
while (true)
{
Console.Write("\nYou: ");
var input = Console.ReadLine();
if (input?.ToLower() == "exit")
break;
if (string.IsNullOrWhiteSpace(input))
continue;
Console.Write("Agent: ");
var response = await agent.ProcessUserRequestAsync(input);
Console.WriteLine(response);
}
}
}
Important Tips for Success
When building Semantic Kernel agents, keep these best practices in mind:
1. Design Clear Function Descriptions
- Use descriptive function names and detailed descriptions
- Provide clear parameter descriptions
- Include examples in your documentation
2. Handle Errors Gracefully
- Always wrap plugin functions in try-catch blocks
- Return meaningful error messages
- Log errors for debugging purposes
3. Optimize Performance
- Use appropriate token limits to control costs
- Implement caching for frequently used data
- Consider using streaming responses for long operations
4. Security Considerations
- Validate all inputs to your plugins
- Implement proper authentication and authorization
- Be cautious with file system access and external API calls
- Never expose sensitive information in function descriptions
5. Testing and Monitoring
- Test your agent with various input scenarios
- Monitor token usage and API costs
- Implement logging to track agent behavior
- Use A/B testing to improve agent responses
Summary
Semantic Kernel agents represent a powerful way to build intelligent applications that can understand natural language, plan actions, and execute tasks autonomously. The example we’ve built demonstrates the core concepts:
- Kernel Configuration: Setting up the AI service and plugins
- Plugin Development: Creating custom functions the agent can use
- Conversation Management: Maintaining context across interactions
- Error Handling: Gracefully managing failures and edge cases
With these foundations, you can extend the agent to support more complex scenarios, integrate with additional APIs, and create sophisticated AI-powered applications that truly understand and assist your users.
The future of software development increasingly involves AI collaboration, and Semantic Kernel provides an excellent framework for building these intelligent partnerships. Start simple, iterate quickly, and gradually add more capabilities as your understanding and requirements grow.
