This example demonstrates enterprise-grade design patterns for building intelligent agents using the Microsoft Agent Framework in .NET with Azure OpenAI (Responses API) integration. You’ll learn professional patterns and architectural approaches that make agents production-ready, maintainable, and scalable.
graph LR
A[IServiceCollection] --> B[Agent Builder]
B --> C[Configuration]
C --> D[Tool Registry]
D --> E[AI Agent]
Ready to build enterprise-grade intelligent agents with .NET? Let’s architect something robust! 🏢✨
az login# zsh/bash
export AZURE_OPENAI_ENDPOINT=https://<your-resource>.openai.azure.com
export AZURE_OPENAI_DEPLOYMENT=gpt-4o-mini
# Then sign in so AzureCliCredential can get a token
az login
# PowerShell
$env:AZURE_OPENAI_ENDPOINT = "https://<your-resource>.openai.azure.com"
$env:AZURE_OPENAI_DEPLOYMENT = "gpt-4o-mini"
# Then sign in so AzureCliCredential can get a token
az login
To run the code example,
# zsh/bash
chmod +x ./03-dotnet-agent-framework.cs
./03-dotnet-agent-framework.cs
Or using the dotnet CLI:
dotnet run ./03-dotnet-agent-framework.cs
See 03-dotnet-agent-framework.cs for the complete code.
#!/usr/bin/dotnet run
#:package Microsoft.Extensions.AI@10.*
#:package Microsoft.Agents.AI.OpenAI@1.*-*
#:package Azure.AI.OpenAI@2.1.0
#:package Azure.Identity@1.13.1
using System.ComponentModel;
using Microsoft.Agents.AI;
using Microsoft.Extensions.AI;
using Azure.AI.OpenAI;
using Azure.Identity;
// Tool Function: Random Destination Generator
// This static method will be available to the agent as a callable tool
// The [Description] attribute helps the AI understand when to use this function
// This demonstrates how to create custom tools for AI agents
[Description("Provides a random vacation destination.")]
static string GetRandomDestination()
{
// List of popular vacation destinations around the world
// The agent will randomly select from these options
var destinations = new List<string>
{
"Paris, France",
"Tokyo, Japan",
"New York City, USA",
"Sydney, Australia",
"Rome, Italy",
"Barcelona, Spain",
"Cape Town, South Africa",
"Rio de Janeiro, Brazil",
"Bangkok, Thailand",
"Vancouver, Canada"
};
// Generate random index and return selected destination
// Uses System.Random for simple random selection
var random = new Random();
int index = random.Next(destinations.Count);
return destinations[index];
}
// Azure OpenAI with the Responses API (stable v1 endpoint). Sign in with `az login`.
var azureEndpoint = Environment.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT")
?? throw new InvalidOperationException("AZURE_OPENAI_ENDPOINT is not set.");
var deployment = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT") ?? "gpt-4o-mini";
var azureClient = new AzureOpenAIClient(new Uri(azureEndpoint), new AzureCliCredential());
// Define Agent Identity and Comprehensive Instructions
// Agent name for identification and logging purposes
var AGENT_NAME = "TravelAgent";
// Detailed instructions that define the agent's personality, capabilities, and behavior
// This system prompt shapes how the agent responds and interacts with users
var AGENT_INSTRUCTIONS = """
You are a helpful AI Agent that can help plan vacations for customers.
Important: When users specify a destination, always plan for that location. Only suggest random destinations when the user hasn't specified a preference.
When the conversation begins, introduce yourself with this message:
"Hello! I'm your TravelAgent assistant. I can help plan vacations and suggest interesting destinations for you. Here are some things you can ask me:
1. Plan a day trip to a specific location
2. Suggest a random vacation destination
3. Find destinations with specific features (beaches, mountains, historical sites, etc.)
4. Plan an alternative trip if you don't like my first suggestion
What kind of trip would you like me to help you plan today?"
Always prioritize user preferences. If they mention a specific destination like "Bali" or "Paris," focus your planning on that location rather than suggesting alternatives.
""";
// Create AI Agent with Advanced Travel Planning Capabilities
// Get the Responses client for the deployment and create the AI agent
// Configure agent with name, detailed instructions, and available tools
// This demonstrates the .NET agent creation pattern with full configuration
AIAgent agent = azureClient
.GetOpenAIResponseClient(deployment)
.CreateAIAgent(
name: AGENT_NAME,
instructions: AGENT_INSTRUCTIONS,
tools: [AIFunctionFactory.Create(GetRandomDestination)]
);
// Create New Conversation Thread for Context Management
// Initialize a new conversation thread to maintain context across multiple interactions
// Threads enable the agent to remember previous exchanges and maintain conversational state
// This is essential for multi-turn conversations and contextual understanding
AgentThread thread = agent.GetNewThread();
// Execute Agent: First Travel Planning Request
// Run the agent with an initial request that will likely trigger the random destination tool
// The agent will analyze the request, use the GetRandomDestination tool, and create an itinerary
// Using the thread parameter maintains conversation context for subsequent interactions
await foreach (var update in agent.RunStreamingAsync("Plan me a day trip", thread))
{
await Task.Delay(10);
Console.Write(update);
}
Console.WriteLine();
// Execute Agent: Follow-up Request with Context Awareness
// Demonstrate contextual conversation by referencing the previous response
// The agent remembers the previous destination suggestion and will provide an alternative
// This showcases the power of conversation threads and contextual understanding in .NET agents
await foreach (var update in agent.RunStreamingAsync("I don't like that destination. Plan me another vacation.", thread))
{
await Task.Delay(10);
Console.Write(update);
}