# Getting started with agents

The getting started with agents samples demonstrate the fundamental concepts and functionalities
of single agents and can be used with any agent type.

While the functionality can be used with any agent type, these samples are configured for
Microsoft Foundry using `AIProjectClient`.

For other samples that demonstrate how to create and configure each type of agent that come with the agent framework,
see the [How to create an agent for each provider](../AgentProviders/README.md) samples.

## Getting started with agents prerequisites

Before you begin, ensure you have the following prerequisites:

- .NET 10 SDK or later
- Microsoft Foundry project endpoint and model configured
- Azure CLI installed and authenticated (for Azure credential authentication)
- User has the required role to invoke models in the Foundry project.

**Note**: These samples use models hosted through Microsoft Foundry. For more information, see [Azure AI Foundry documentation](https://learn.microsoft.com/en-us/azure/ai-foundry/).

**Note**: These samples use Azure CLI credentials for authentication. Make sure you're logged in with `az login` and have access to the Foundry project. For more information, see the [Azure CLI documentation](https://learn.microsoft.com/cli/azure/authenticate-azure-cli-interactively).

## Samples

|Sample|Description|
|---|---|
|[Using OpenAPI function tools with a simple agent](https://github.com/microsoft/semantic-kernel/tree/main/dotnet/samples/AgentFrameworkMigration/AzureOpenAI/Step04_ToolCall_WithOpenAPI)|This sample demonstrates how to create function tools from an OpenAPI spec and use them with a simple agent (note that this sample is in the Semantic Kernel repository)|
|[Using function tools with approvals](./Agent_Step01_UsingFunctionToolsWithApprovals/)|This sample demonstrates how to use function tools where approvals require human in the loop approvals before execution|
|[Structured output with a simple agent](./Agent_Step02_StructuredOutput/)|This sample demonstrates how to use structured output with a simple agent|
|[Persisted conversations with a simple agent](./Agent_Step03_PersistedConversations/)|This sample demonstrates how to persist conversations and reload them later. This is useful for cases where an agent is hosted in a stateless service|
|[3rd party chat history storage with a simple agent](./Agent_Step04_3rdPartyChatHistoryStorage/)|This sample demonstrates how to store chat history in a 3rd party storage solution|
|[Observability with a simple agent](./Agent_Step05_Observability/)|This sample demonstrates how to add telemetry to a simple agent|
|[Dependency injection with a simple agent](./Agent_Step06_DependencyInjection/)|This sample demonstrates how to add and resolve an agent with a dependency injection container|
|[Exposing a simple agent as MCP tool](./Agent_Step07_AsMcpTool/)|This sample demonstrates how to expose an agent as an MCP tool|
|[Using images with a simple agent](./Agent_Step08_UsingImages/)|This sample demonstrates how to use image multi-modality with an AI agent|
|[Exposing a simple agent as a function tool](./Agent_Step09_AsFunctionTool/)|This sample demonstrates how to expose an agent as a function tool|
|[Background responses with tools and persistence](./Agent_Step10_BackgroundResponsesWithToolsAndPersistence/)|This sample demonstrates advanced background response scenarios including function calling during background operations and state persistence|
|[Using middleware with an agent](./Agent_Step11_Middleware/)|This sample demonstrates how to use middleware with an agent|
|[Using plugins with an agent](./Agent_Step12_Plugins/)|This sample demonstrates how to use plugins with an agent|
|[Reducing chat history size](./Agent_Step13_ChatReduction/)|This sample demonstrates how to reduce the chat history to constrain its size, where chat history is maintained locally|
|[Background responses](./Agent_Step14_BackgroundResponses/)|This sample demonstrates how to use background responses for long-running operations with polling and resumption support|
|[Deep research with an agent](./Agent_Step15_DeepResearch/)|This sample demonstrates how to use the Deep Research Tool to perform comprehensive research on complex topics|
|[Declarative agent](./Agent_Step16_Declarative/)|This sample demonstrates how to declaratively define an agent.|
|[Providing additional AI Context to an agent using multiple AIContextProviders](./Agent_Step17_AdditionalAIContext/)|This sample demonstrates how to inject additional AI context into a ChatClientAgent using multiple custom AIContextProvider components that are attached to the agent.|
|[Using compaction pipeline with an agent](./Agent_Step18_CompactionPipeline/)|This sample demonstrates how to use a compaction pipeline to efficiently limit the size of the conversation history for an agent.|
|[In-function-loop checkpointing](./Agent_Step19_InFunctionLoopCheckpointing/)|This sample demonstrates how to persist chat history after each service call during a tool-calling loop, enabling crash recovery and mid-run observability.|
|[Dynamic function tools](./Agent_Step20_DynamicFunctionTools/)|This sample demonstrates how to dynamically expand the set of function tools available to an agent during a function-calling loop using the ambient FunctionInvocationContext.|

## Running the samples from the console

To run the samples, navigate to the desired sample directory, e.g.

```powershell
cd Agent_Step01_UsingFunctionToolsWithApprovals
```

Set the following environment variables:

```powershell
$env:FOUNDRY_PROJECT_ENDPOINT="https://<your-project>.services.ai.azure.com/api/projects/<your-project>" # Replace with your Foundry project endpoint
$env:FOUNDRY_MODEL="gpt-5.4-mini"  # Optional, defaults to gpt-5.4-mini
```

If the variables are not set, you will be prompted for the values when running the samples.

Execute the following command to build the sample:

```powershell
dotnet build
```

Execute the following command to run the sample:

```powershell
dotnet run --no-build
```

Or just build and run in one step:

```powershell
dotnet run
```

## Running the samples from Visual Studio

Open the solution in Visual Studio and set the desired sample project as the startup project. Then, run the project using the built-in debugger or by pressing `F5`.

You will be prompted for any required environment variables if they are not already set.
