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<Project Sdk="Microsoft.NET.Sdk">
<PropertyGroup>
<OutputType>Exe</OutputType>
<TargetFrameworks>net10.0</TargetFrameworks>
<Nullable>enable</Nullable>
<ImplicitUsings>enable</ImplicitUsings>
</PropertyGroup>
<ItemGroup>
<PackageReference Include="Azure.AI.Projects" />
<PackageReference Include="Azure.Identity" />
</ItemGroup>
<ItemGroup>
<ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.AzureAI\Microsoft.Agents.AI.AzureAI.csproj" />
</ItemGroup>
</Project>

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// Copyright (c) Microsoft. All rights reserved.
// This sample shows how to configure an agent to produce structured output.
using System.ComponentModel;
using System.Text.Json;
using System.Text.Json.Serialization;
using Azure.AI.Projects;
using Azure.Identity;
using Microsoft.Agents.AI;
using SampleApp;
#pragma warning disable CA5399
string endpoint = Environment.GetEnvironmentVariable("AZURE_FOUNDRY_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("AZURE_FOUNDRY_PROJECT_ENDPOINT is not set.");
string deploymentName = Environment.GetEnvironmentVariable("AZURE_FOUNDRY_PROJECT_DEPLOYMENT_NAME") ?? "gpt-4o-mini";
const string AssistantInstructions = "You are a helpful assistant that extracts structured information about people.";
const string AssistantName = "StructuredOutputAssistant";
// Get a client to create/retrieve/delete server side agents with Azure Foundry Agents.
AIProjectClient aiProjectClient = new(new Uri(endpoint), new AzureCliCredential());
// Create ChatClientAgent directly
ChatClientAgent agent = await aiProjectClient.CreateAIAgentAsync(
model: deploymentName,
new ChatClientAgentOptions()
{
Name = AssistantName,
ChatOptions = new()
{
Instructions = AssistantInstructions,
ResponseFormat = Microsoft.Extensions.AI.ChatResponseFormat.ForJsonSchema<PersonInfo>()
}
});
// Set PersonInfo as the type parameter of RunAsync method to specify the expected structured output from the agent and invoke the agent with some unstructured input.
AgentResponse<PersonInfo> response = await agent.RunAsync<PersonInfo>("Please provide information about John Smith, who is a 35-year-old software engineer.");
// Access the structured output via the Result property of the agent response.
Console.WriteLine("Assistant Output:");
Console.WriteLine($"Name: {response.Result.Name}");
Console.WriteLine($"Age: {response.Result.Age}");
Console.WriteLine($"Occupation: {response.Result.Occupation}");
// Create the ChatClientAgent with the specified name, instructions, and expected structured output the agent should produce.
ChatClientAgent agentWithPersonInfo = await aiProjectClient.CreateAIAgentAsync(
model: deploymentName,
new ChatClientAgentOptions()
{
Name = AssistantName,
ChatOptions = new()
{
Instructions = AssistantInstructions,
ResponseFormat = Microsoft.Extensions.AI.ChatResponseFormat.ForJsonSchema<PersonInfo>()
}
});
// Invoke the agent with some unstructured input while streaming, to extract the structured information from.
IAsyncEnumerable<AgentResponseUpdate> updates = agentWithPersonInfo.RunStreamingAsync("Please provide information about John Smith, who is a 35-year-old software engineer.");
// Assemble all the parts of the streamed output, since we can only deserialize once we have the full json,
// then deserialize the response into the PersonInfo class.
PersonInfo personInfo = (await updates.ToAgentResponseAsync()).Deserialize<PersonInfo>(JsonSerializerOptions.Web);
Console.WriteLine("Assistant Output:");
Console.WriteLine($"Name: {personInfo.Name}");
Console.WriteLine($"Age: {personInfo.Age}");
Console.WriteLine($"Occupation: {personInfo.Occupation}");
// Cleanup by agent name removes the agent version created.
await aiProjectClient.Agents.DeleteAgentAsync(agent.Name);
namespace SampleApp
{
/// <summary>
/// Represents information about a person, including their name, age, and occupation, matched to the JSON schema used in the agent.
/// </summary>
[Description("Information about a person including their name, age, and occupation")]
public class PersonInfo
{
[JsonPropertyName("name")]
public string? Name { get; set; }
[JsonPropertyName("age")]
public int? Age { get; set; }
[JsonPropertyName("occupation")]
public string? Occupation { get; set; }
}
}

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# Structured Output with AI Agents
This sample demonstrates how to configure AI agents to produce structured output in JSON format using JSON schemas.
## What this sample demonstrates
- Configuring agents with JSON schema response formats
- Using generic RunAsync<T> method for structured output
- Deserializing structured responses into typed objects
- Running agents with streaming and structured output
- Managing agent lifecycle (creation and deletion)
## Prerequisites
Before you begin, ensure you have the following prerequisites:
- .NET 10 SDK or later
- Azure Foundry service endpoint and deployment configured
- Azure CLI installed and authenticated (for Azure credential authentication)
**Note**: This demo uses Azure CLI credentials for authentication. Make sure you're logged in with `az login` and have access to the Azure Foundry resource. For more information, see the [Azure CLI documentation](https://learn.microsoft.com/cli/azure/authenticate-azure-cli-interactively).
Set the following environment variables:
```powershell
$env:AZURE_FOUNDRY_PROJECT_ENDPOINT="https://your-foundry-service.services.ai.azure.com/api/projects/your-foundry-project" # Replace with your Azure Foundry resource endpoint
$env:AZURE_FOUNDRY_PROJECT_DEPLOYMENT_NAME="gpt-4o-mini" # Optional, defaults to gpt-4o-mini
```
## Run the sample
Navigate to the FoundryAgents sample directory and run:
```powershell
cd dotnet/samples/GettingStarted/FoundryAgents
dotnet run --project .\FoundryAgents_Step05_StructuredOutput
```
## Expected behavior
The sample will:
1. Create an agent named "StructuredOutputAssistant" configured to produce JSON output
2. Run the agent with a prompt to extract person information
3. Deserialize the JSON response into a PersonInfo object
4. Display the structured data (Name, Age, Occupation)
5. Run the agent again with streaming and deserialize the streamed JSON response
6. Clean up resources by deleting the agent