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Microsoft Semantic Kernel

Master Semantic Kernel for building enterprise-grade AI agents with plugin architecture

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Production-Ready Enterprise Features

Semantic Kernel is built for enterprise production workloads with comprehensive logging, telemetry, security, and native Azure integration. Deploy AI agents with confidence.

📊 Logging & Observability

Built-in Logging with ILogger
using Microsoft.Extensions.Logging;

// Configure logging
var loggerFactory = LoggerFactory.Create(builder =>
{
    builder.AddConsole();
    builder.AddApplicationInsights(instrumentationKey);
    builder.SetMinimumLevel(LogLevel.Information);
});

// Kernel automatically logs:
// - Function invocations and results
// - Planner decisions and reasoning
// - LLM calls and token usage
// - Errors and retries
var kernel = Kernel.CreateBuilder()
    .AddAzureOpenAIChatCompletion(endpoint, apiKey, model)
    .Build(loggerFactory);

🔍 What Gets Logged

  • • Function execution traces
  • • LLM requests/responses
  • • Token usage per call
  • • Planner reasoning steps
  • • Memory operations
  • • Errors with stack traces

📈 Application Insights

  • • Request telemetry dashboards
  • • Performance metrics
  • • Dependency tracking
  • • Custom events & metrics
  • • Alert configuration
  • • Cost analysis

🔐 Security & Managed Identity

Azure Managed Identity (No API Keys!)
using Azure.Identity;

// No hardcoded secrets - use Managed Identity
var credential = new DefaultAzureCredential();

var kernel = Kernel.CreateBuilder()
    .AddAzureOpenAIChatCompletion(
        endpoint: "https://your-openai.openai.azure.com/",
        credential: credential,  // ← Automatic Azure AD authentication
        modelId: "gpt-4"
    )
    .Build();

// Also works for Key Vault, Storage, Cosmos DB, etc.
// Identity is managed by Azure, no secrets in code
Azure Key Vault Integration
using Azure.Security.KeyVault.Secrets;

// Store secrets in Key Vault
var client = new SecretClient(
    new Uri("https://your-vault.vault.azure.net/"),
    new DefaultAzureCredential()
);

var apiKey = await client.GetSecretAsync("OpenAI-ApiKey");

var kernel = Kernel.CreateBuilder()
    .AddOpenAIChatCompletion(
        modelId: "gpt-4",
        apiKey: apiKey.Value.Value  // ← From Key Vault, not code
    )
    .Build();

☁️ Native Azure Integration

🤖 Azure OpenAI Service

First-class support for Azure OpenAI with enterprise SLAs

kernel.AddAzureOpenAIChatCompletion(endpoint, credential, model)

🔍 Azure Cognitive Search

Semantic memory with enterprise-grade vector search

memory.WithAzureCognitiveSearchMemoryStore(endpoint, credential)

🗄️ Azure Cosmos DB

Persist kernel state and conversation history globally

services.AddSingleton<IKernelMemoryStore>(new CosmosDbStore(...))

⚡ Resilience & Retry Policies

Polly Integration for Retries
using Polly;

// Configure retry with exponential backoff
var retryPolicy = Policy
    .Handle<HttpRequestException>()
    .WaitAndRetryAsync(3, retryAttempt => 
        TimeSpan.FromSeconds(Math.Pow(2, retryAttempt))
    );

// Apply to kernel HTTP client
var kernel = Kernel.CreateBuilder()
    .AddAzureOpenAIChatCompletion(
        endpoint,
        apiKey,
        model,
        httpClient: new HttpClient(
            new PolicyHttpMessageHandler(retryPolicy)
        )
    )
    .Build();

// Automatically retries on transient failures (rate limits, timeouts)

💰 Cost Management

Track Token Usage

var result = await kernel
    .InvokeAsync(...);
    
// Access token metadata
var usage = result
    .Metadata?["Usage"];
Console.WriteLine(
  $"Tokens: {usage}"
);

Set Token Limits

var settings = new() {
    MaxTokens = 500,
    Temperature = 0.7
};

await kernel.InvokeAsync(
    ...,
    settings
);

🏢 Enterprise Deployment Checklist

  • ✓Use Managed Identity - No API keys in code or config files
  • ✓Enable Application Insights - Monitor performance, costs, and errors
  • ✓Configure retry policies - Handle rate limits and transient failures
  • ✓Set token limits - Prevent runaway costs from LLM calls
  • ✓Use Key Vault - Centralize secret management
  • ✓Test at scale - Load test with realistic workloads
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