agent-framework/dotnet/samples/GettingStarted/FoundryAgents

Getting started with Foundry Agents

The getting started with Foundry Agents samples demonstrate the fundamental concepts and functionalities of Azure Foundry Agents and can be used with Azure Foundry as the AI provider.

These samples showcase how to work with agents managed through Azure Foundry, including agent creation, versioning, multi-turn conversations, and advanced features like code interpretation and computer use.

Classic vs New Foundry Agents

Note

Recently, Azure Foundry introduced a new and improved experience for creating and managing AI agents, which is the target of these samples.

For more information about the previous classic agents and for what's new in Foundry Agents, see the Foundry Agents migration documentation.

For a sample demonstrating how to use classic Foundry Agents, see the following: Agent with Azure AI Persistent.

Agent Versioning and Static Definitions

One of the key architectural changes in the new Foundry Agents compared to the classic experience is how agent definitions are handled. In the new architecture, agents have versions and their definitions are established at creation time. This means that the agent's configuration—including instructions, tools, and options—is fixed when the agent version is created.

Important

Agent versions are static and strictly adhere to their original definition. Any attempt to provide or override tools, instructions, or options during an agent run or request will be ignored by the agent, as the API does not support runtime configuration changes. All agent behavior must be defined at agent creation time.

This design ensures consistency and predictability in agent behavior across all interactions with a specific agent version.

The Agent Framework intentionally ignores unsupported runtime parameters rather than throwing exceptions. This abstraction-first approach ensures that code written against the unified agent abstraction remains portable across providers (OpenAI, Azure OpenAI, Foundry Agents). It removes the need for provider-specific conditional logic. Teams can adopt Foundry Agents without rewriting existing orchestration code. Configurations that work with other providers will gracefully degrade, rather than fail, when the underlying API does not support them.

Getting started with Foundry Agents prerequisites

Before you begin, ensure you have the following prerequisites:

• .NET 10 SDK or later
• Azure Foundry service endpoint and project configured
• Azure CLI installed and authenticated (for Azure credential authentication)

Note: These samples use Azure Foundry Agents. For more information, see Azure AI Foundry documentation.

Note: These samples use 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.

Samples

Sample	Description
Basics	This sample demonstrates how to create and manage AI agents with versioning
Running a simple agent	This sample demonstrates how to create and run a basic Foundry agent
Multi-turn conversation	This sample demonstrates how to implement a multi-turn conversation with a Foundry agent
Using function tools	This sample demonstrates how to use function tools with a Foundry agent
Using function tools with approvals	This sample demonstrates how to use function tools where approvals require human in the loop approvals before execution
Structured output	This sample demonstrates how to use structured output with a Foundry agent
Persisted conversations	This sample demonstrates how to persist conversations and reload them later
Observability	This sample demonstrates how to add telemetry to a Foundry agent
Dependency injection	This sample demonstrates how to add and resolve a Foundry agent with a dependency injection container
Using MCP client as tools	This sample demonstrates how to use MCP clients as tools with a Foundry agent
Using images	This sample demonstrates how to use image multi-modality with a Foundry agent
Exposing as a function tool	This sample demonstrates how to expose a Foundry agent as a function tool
Using middleware	This sample demonstrates how to use middleware with a Foundry agent
Using plugins	This sample demonstrates how to use plugins with a Foundry agent
Code interpreter	This sample demonstrates how to use the code interpreter tool with a Foundry agent
Computer use	This sample demonstrates how to use computer use capabilities with a Foundry agent
File search	This sample demonstrates how to use the file search tool with a Foundry agent
Local MCP	This sample demonstrates how to use a local MCP client with a Foundry agent

Evaluation Samples

Evaluation is critical for building trustworthy and high-quality AI applications. The evaluation samples demonstrate how to assess agent safety, quality, and performance using Azure AI Foundry's evaluation capabilities.

Sample	Description
Red Team Evaluation	This sample demonstrates how to use Azure AI Foundry's Red Teaming service to assess model safety against adversarial attacks
Self-Reflection with Groundedness	This sample demonstrates the self-reflection pattern where agents iteratively improve responses based on groundedness evaluation

For details on safety evaluation, see the Red Team Evaluation README.

Running the samples from the console

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

cd FoundryAgents_Step01.2_Running

Set the following environment variables:

$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

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:

dotnet build

Execute the following command to run the sample:

dotnet run --no-build

Or just build and run in one step:

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.