# Creating and Running a Basic Agent with the Responses API

This sample demonstrates how to create and run a basic AI agent using the `ChatClientAgent`, which uses the Microsoft Foundry Responses API directly without creating server-side agent definitions.

## What this sample demonstrates

- Creating a `ChatClientAgent` with instructions and a model
- Running a simple single-turn conversation
- No server-side agent creation or cleanup required

## Prerequisites

Before you begin, ensure you have the following prerequisites:

- .NET 10 SDK or later
- Microsoft 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 Microsoft 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_AI_PROJECT_ENDPOINT="https://your-foundry-service.services.ai.azure.com/api/projects/your-foundry-project"
$env:AZURE_AI_MODEL_DEPLOYMENT_NAME="gpt-5.4-mini"
```

## Run the sample

Navigate to the AgentsWithFoundry sample directory and run:

```powershell
cd dotnet/samples/02-agents/AgentsWithFoundry
dotnet run --project .\Agent_Step01_Basics
```

## Alternative: Composable approach

You can also create the same agent by composing the underlying `IChatClient` directly. This gives you full control over the chat client pipeline:

```csharp
using Azure.AI.Projects;
using Azure.Identity;
using Microsoft.Agents.AI;
using Microsoft.Extensions.AI;

AIProjectClient aiProjectClient = new(new Uri(endpoint), new DefaultAzureCredential());

AIAgent agent = new ChatClientAgent(
    chatClient: aiProjectClient.GetProjectOpenAIClient().GetProjectResponsesClient().AsIChatClient(deploymentName),
    instructions: "You are good at telling jokes.",
    name: "JokerAgent");
```

This approach is useful when you need to customize the chat client pipeline or swap providers (e.g., Anthropic, OpenAI) while keeping the same agent code.