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This commit is contained in:
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parent f78f2388b3
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<Project Sdk="Microsoft.NET.Sdk">
<PropertyGroup>
<OutputType>Exe</OutputType>
<TargetFrameworks>net10.0</TargetFrameworks>
<Nullable>enable</Nullable>
<ImplicitUsings>enable</ImplicitUsings>
<!--
Disable central package management for this project.
This project requires explicit package references with versions specified inline rather than
inheriting them from Directory.Packages.props. This is necessary because a Docker image will
be created from this project, and the Docker build process only has access to this folder
and cannot access parent folders where Directory.Packages.props resides.
-->
<ManagePackageVersionsCentrally>false</ManagePackageVersionsCentrally>
<NoWarn>$(NoWarn);MEAI001;OPENAI001</NoWarn>
</PropertyGroup>
<!--
Remove analyzer PackageReference items inherited from Directory.Packages.props.
Note: ManagePackageVersionsCentrally only controls PackageVersion items, not PackageReference items.
Directory.Packages.props contains both PackageVersion and PackageReference entries for analyzers,
and the PackageReference items are always inherited through MSBuild imports regardless of the
ManagePackageVersionsCentrally setting. We must explicitly remove them before adding our own versions.
-->
<ItemGroup>
<PackageReference Remove="Microsoft.CodeAnalysis.NetAnalyzers" />
<PackageReference Remove="Microsoft.VisualStudio.Threading.Analyzers" />
<PackageReference Remove="xunit.analyzers" />
<PackageReference Remove="Moq.Analyzers" />
<PackageReference Remove="Roslynator.Analyzers" />
<PackageReference Remove="Roslynator.CodeAnalysis.Analyzers" />
<PackageReference Remove="Roslynator.Formatting.Analyzers" />
</ItemGroup>
<ItemGroup>
<PackageReference Include="Azure.AI.AgentServer.AgentFramework" Version="1.0.0-beta.5" />
<PackageReference Include="Azure.AI.OpenAI" Version="2.7.0-beta.2" />
<PackageReference Include="Azure.Identity" Version="1.17.1" />
<PackageReference Include="Microsoft.Agents.AI.OpenAI" Version="1.0.0-preview.251219.1" />
<PackageReference Include="Microsoft.Extensions.AI.OpenAI" Version="10.1.1-preview.1.25612.2" />
</ItemGroup>
<!-- Add analyzers with compatible versions -->
<ItemGroup>
<PackageReference Include="Microsoft.CodeAnalysis.NetAnalyzers" Version="10.0.100">
<PrivateAssets>all</PrivateAssets>
<IncludeAssets>runtime; build; native; contentfiles; analyzers; buildtransitive</IncludeAssets>
</PackageReference>
<PackageReference Include="Microsoft.VisualStudio.Threading.Analyzers" Version="17.14.15">
<PrivateAssets>all</PrivateAssets>
<IncludeAssets>runtime; build; native; contentfiles; analyzers; buildtransitive</IncludeAssets>
</PackageReference>
<PackageReference Include="Roslynator.Analyzers" Version="4.14.1">
<PrivateAssets>all</PrivateAssets>
<IncludeAssets>runtime; build; native; contentfiles; analyzers; buildtransitive</IncludeAssets>
</PackageReference>
<PackageReference Include="Roslynator.CodeAnalysis.Analyzers" Version="4.14.1">
<PrivateAssets>all</PrivateAssets>
<IncludeAssets>runtime; build; native; contentfiles; analyzers; buildtransitive</IncludeAssets>
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<PackageReference Include="Roslynator.Formatting.Analyzers" Version="4.14.1">
<PrivateAssets>all</PrivateAssets>
<IncludeAssets>runtime; build; native; contentfiles; analyzers; buildtransitive</IncludeAssets>
</PackageReference>
</ItemGroup>
</Project>

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# Build the application
FROM mcr.microsoft.com/dotnet/sdk:10.0-alpine AS build
WORKDIR /src
# Copy files from the current directory on the host to the working directory in the container
COPY . .
RUN dotnet restore
RUN dotnet build -c Release --no-restore
RUN dotnet publish -c Release --no-build -o /app -f net10.0
# Run the application
FROM mcr.microsoft.com/dotnet/aspnet:10.0-alpine AS final
WORKDIR /app
# Copy everything needed to run the app from the "build" stage.
COPY --from=build /app .
EXPOSE 8088
ENTRYPOINT ["dotnet", "AgentWithHostedMCP.dll"]

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// Copyright (c) Microsoft. All rights reserved.
// This sample shows how to create and use a simple AI agent with OpenAI Responses as the backend, that uses a Hosted MCP Tool.
// In this case the OpenAI responses service will invoke any MCP tools as required. MCP tools are not invoked by the Agent Framework.
// The sample demonstrates how to use MCP tools with auto approval by setting ApprovalMode to NeverRequire.
using Azure.AI.AgentServer.AgentFramework.Extensions;
using Azure.AI.OpenAI;
using Azure.Identity;
using Microsoft.Agents.AI;
using Microsoft.Extensions.AI;
using OpenAI.Responses;
var endpoint = Environment.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT") ?? throw new InvalidOperationException("AZURE_OPENAI_ENDPOINT is not set.");
var deploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT_NAME") ?? "gpt-4o-mini";
// Create an MCP tool that can be called without approval.
AITool mcpTool = new HostedMcpServerTool(serverName: "microsoft_learn", serverAddress: "https://learn.microsoft.com/api/mcp")
{
AllowedTools = ["microsoft_docs_search"],
ApprovalMode = HostedMcpServerToolApprovalMode.NeverRequire
};
// Create an agent with the MCP tool using Azure OpenAI Responses.
AIAgent agent = new AzureOpenAIClient(
new Uri(endpoint),
new DefaultAzureCredential())
.GetResponsesClient(deploymentName)
.CreateAIAgent(
instructions: "You answer questions by searching the Microsoft Learn content only.",
name: "MicrosoftLearnAgent",
tools: [mcpTool]);
await agent.RunAIAgentAsync();

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# What this sample demonstrates
This sample demonstrates how to use a Hosted Model Context Protocol (MCP) server with an AI agent.
The agent connects to the Microsoft Learn MCP server to search documentation and answer questions using official Microsoft content.
Key features:
- Configuring MCP tools with automatic approval (no user confirmation required)
- Filtering available tools from an MCP server
- Using Azure OpenAI Responses with MCP tools
## Prerequisites
Before running this sample, ensure you have:
1. An Azure OpenAI endpoint configured
2. A deployment of a chat model (e.g., gpt-4o-mini)
3. Azure CLI installed and authenticated
**Note**: This sample uses Azure CLI credentials for authentication. Make sure you're logged in with `az login` and have access to the Azure OpenAI resource.
## Environment Variables
Set the following environment variables:
```powershell
# Replace with your Azure OpenAI endpoint
$env:AZURE_OPENAI_ENDPOINT="https://your-openai-resource.openai.azure.com/"
# Optional, defaults to gpt-4o-mini
$env:AZURE_OPENAI_DEPLOYMENT_NAME="gpt-4o-mini"
```
## How It Works
The sample connects to the Microsoft Learn MCP server and uses its documentation search capabilities:
1. The agent is configured with a HostedMcpServerTool pointing to `https://learn.microsoft.com/api/mcp`
2. Only the `microsoft_docs_search` tool is enabled from the available MCP tools
3. Approval mode is set to `NeverRequire`, allowing automatic tool execution
4. When you ask questions, Azure OpenAI Responses automatically invokes the MCP tool to search documentation
5. The agent returns answers based on the Microsoft Learn content
In this configuration, the OpenAI Responses service manages tool invocation directly - the Agent Framework does not handle MCP tool calls.

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name: AgentWithHostedMCP
displayName: "Microsoft Learn Response Agent with MCP"
description: >
An AI agent that uses Azure OpenAI Responses with a Hosted Model Context Protocol (MCP) server.
The agent answers questions by searching Microsoft Learn documentation using MCP tools.
This demonstrates how MCP tools can be integrated with Azure OpenAI Responses where the service
itself handles tool invocation.
metadata:
authors:
- Microsoft Agent Framework Team
tags:
- Azure AI AgentServer
- Microsoft Agent Framework
- Model Context Protocol
- MCP
- Tool Call Approval
template:
kind: hosted
name: AgentWithHostedMCP
protocols:
- protocol: responses
version: v1
environment_variables:
- name: AZURE_OPENAI_ENDPOINT
value: ${AZURE_OPENAI_ENDPOINT}
- name: AZURE_OPENAI_DEPLOYMENT_NAME
value: gpt-4o-mini
resources:
- name: "gpt-4o-mini"
kind: model
id: gpt-4o-mini

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@host = http://localhost:8088
@endpoint = {{host}}/responses
### Health Check
GET {{host}}/readiness
### Simple string input - Ask about MCP Tools
POST {{endpoint}}
Content-Type: application/json
{
"input": "Please summarize the Azure AI Agent documentation related to MCP Tool calling?"
}
### Explicit input - Ask about Agent Framework
POST {{endpoint}}
Content-Type: application/json
{
"input": [
{
"type": "message",
"role": "user",
"content": [
{
"type": "input_text",
"text": "What is the Microsoft Agent Framework?"
}
]
}
]
}

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@@ -0,0 +1,69 @@
<Project Sdk="Microsoft.NET.Sdk">
<PropertyGroup>
<OutputType>Exe</OutputType>
<TargetFrameworks>net10.0</TargetFrameworks>
<Nullable>enable</Nullable>
<ImplicitUsings>enable</ImplicitUsings>
<!--
Disable central package management for this project.
This project requires explicit package references with versions specified inline rather than
inheriting them from Directory.Packages.props. This is necessary because a Docker image will
be created from this project, and the Docker build process only has access to this folder
and cannot access parent folders where Directory.Packages.props resides.
-->
<ManagePackageVersionsCentrally>false</ManagePackageVersionsCentrally>
</PropertyGroup>
<!--
Remove analyzer PackageReference items inherited from Directory.Packages.props.
Note: ManagePackageVersionsCentrally only controls PackageVersion items, not PackageReference items.
Directory.Packages.props contains both PackageVersion and PackageReference entries for analyzers,
and the PackageReference items are always inherited through MSBuild imports regardless of the
ManagePackageVersionsCentrally setting. We must explicitly remove them before adding our own versions.
-->
<ItemGroup>
<PackageReference Remove="Microsoft.CodeAnalysis.NetAnalyzers" />
<PackageReference Remove="Microsoft.VisualStudio.Threading.Analyzers" />
<PackageReference Remove="xunit.analyzers" />
<PackageReference Remove="Moq.Analyzers" />
<PackageReference Remove="Roslynator.Analyzers" />
<PackageReference Remove="Roslynator.CodeAnalysis.Analyzers" />
<PackageReference Remove="Roslynator.Formatting.Analyzers" />
</ItemGroup>
<ItemGroup>
<PackageReference Include="Azure.AI.AgentServer.AgentFramework" Version="1.0.0-beta.5" />
<PackageReference Include="Azure.AI.OpenAI" Version="2.7.0-beta.2" />
<PackageReference Include="Azure.Identity" Version="1.17.1" />
<PackageReference Include="Microsoft.Agents.AI.OpenAI" Version="1.0.0-preview.251219.1" />
<PackageReference Include="Microsoft.Extensions.AI.OpenAI" Version="10.1.1-preview.1.25612.2" />
</ItemGroup>
<!-- Add analyzers with compatible versions -->
<ItemGroup>
<PackageReference Include="Microsoft.CodeAnalysis.NetAnalyzers" Version="10.0.100">
<PrivateAssets>all</PrivateAssets>
<IncludeAssets>runtime; build; native; contentfiles; analyzers; buildtransitive</IncludeAssets>
</PackageReference>
<PackageReference Include="Microsoft.VisualStudio.Threading.Analyzers" Version="17.14.15">
<PrivateAssets>all</PrivateAssets>
<IncludeAssets>runtime; build; native; contentfiles; analyzers; buildtransitive</IncludeAssets>
</PackageReference>
<PackageReference Include="Roslynator.Analyzers" Version="4.14.1">
<PrivateAssets>all</PrivateAssets>
<IncludeAssets>runtime; build; native; contentfiles; analyzers; buildtransitive</IncludeAssets>
</PackageReference>
<PackageReference Include="Roslynator.CodeAnalysis.Analyzers" Version="4.14.1">
<PrivateAssets>all</PrivateAssets>
<IncludeAssets>runtime; build; native; contentfiles; analyzers; buildtransitive</IncludeAssets>
</PackageReference>
<PackageReference Include="Roslynator.Formatting.Analyzers" Version="4.14.1">
<PrivateAssets>all</PrivateAssets>
<IncludeAssets>runtime; build; native; contentfiles; analyzers; buildtransitive</IncludeAssets>
</PackageReference>
</ItemGroup>
</Project>

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@@ -0,0 +1,20 @@
# Build the application
FROM mcr.microsoft.com/dotnet/sdk:10.0-alpine AS build
WORKDIR /src
# Copy files from the current directory on the host to the working directory in the container
COPY . .
RUN dotnet restore
RUN dotnet build -c Release --no-restore
RUN dotnet publish -c Release --no-build -o /app -f net10.0
# Run the application
FROM mcr.microsoft.com/dotnet/aspnet:10.0-alpine AS final
WORKDIR /app
# Copy everything needed to run the app from the "build" stage.
COPY --from=build /app .
EXPOSE 8088
ENTRYPOINT ["dotnet", "AgentWithTextSearchRag.dll"]

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// Copyright (c) Microsoft. All rights reserved.
// This sample shows how to use TextSearchProvider to add retrieval augmented generation (RAG)
// capabilities to an AI agent. The provider runs a search against an external knowledge base
// before each model invocation and injects the results into the model context.
using Azure.AI.AgentServer.AgentFramework.Extensions;
using Azure.AI.OpenAI;
using Azure.Identity;
using Microsoft.Agents.AI;
using Microsoft.Extensions.AI;
using OpenAI.Chat;
var endpoint = Environment.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT") ?? throw new InvalidOperationException("AZURE_OPENAI_ENDPOINT is not set.");
var deploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT_NAME") ?? "gpt-4o-mini";
TextSearchProviderOptions textSearchOptions = new()
{
// Run the search prior to every model invocation and keep a short rolling window of conversation context.
SearchTime = TextSearchProviderOptions.TextSearchBehavior.BeforeAIInvoke,
RecentMessageMemoryLimit = 6,
};
AIAgent agent = new AzureOpenAIClient(
new Uri(endpoint),
new DefaultAzureCredential())
.GetChatClient(deploymentName)
.CreateAIAgent(new ChatClientAgentOptions
{
ChatOptions = new ChatOptions
{
Instructions = "You are a helpful support specialist for Contoso Outdoors. Answer questions using the provided context and cite the source document when available.",
},
AIContextProviderFactory = ctx => new TextSearchProvider(MockSearchAsync, ctx.SerializedState, ctx.JsonSerializerOptions, textSearchOptions)
});
await agent.RunAIAgentAsync();
static Task<IEnumerable<TextSearchProvider.TextSearchResult>> MockSearchAsync(string query, CancellationToken cancellationToken)
{
// The mock search inspects the user's question and returns pre-defined snippets
// that resemble documents stored in an external knowledge source.
List<TextSearchProvider.TextSearchResult> results = [];
if (query.Contains("return", StringComparison.OrdinalIgnoreCase) || query.Contains("refund", StringComparison.OrdinalIgnoreCase))
{
results.Add(new()
{
SourceName = "Contoso Outdoors Return Policy",
SourceLink = "https://contoso.com/policies/returns",
Text = "Customers may return any item within 30 days of delivery. Items should be unused and include original packaging. Refunds are issued to the original payment method within 5 business days of inspection."
});
}
if (query.Contains("shipping", StringComparison.OrdinalIgnoreCase))
{
results.Add(new()
{
SourceName = "Contoso Outdoors Shipping Guide",
SourceLink = "https://contoso.com/help/shipping",
Text = "Standard shipping is free on orders over $50 and typically arrives in 3-5 business days within the continental United States. Expedited options are available at checkout."
});
}
if (query.Contains("tent", StringComparison.OrdinalIgnoreCase) || query.Contains("fabric", StringComparison.OrdinalIgnoreCase))
{
results.Add(new()
{
SourceName = "TrailRunner Tent Care Instructions",
SourceLink = "https://contoso.com/manuals/trailrunner-tent",
Text = "Clean the tent fabric with lukewarm water and a non-detergent soap. Allow it to air dry completely before storage and avoid prolonged UV exposure to extend the lifespan of the waterproof coating."
});
}
return Task.FromResult<IEnumerable<TextSearchProvider.TextSearchResult>>(results);
}

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# What this sample demonstrates
This sample demonstrates how to use TextSearchProvider to add retrieval augmented generation (RAG) capabilities to an AI agent. The provider runs a search against an external knowledge base before each model invocation and injects the results into the model context.
Key features:
- Configuring TextSearchProvider with custom search behavior
- Running searches before AI invocations to provide relevant context
- Managing conversation memory with a rolling window approach
- Citing source documents in AI responses
## Prerequisites
Before running this sample, ensure you have:
1. An Azure OpenAI endpoint configured
2. A deployment of a chat model (e.g., gpt-4o-mini)
3. Azure CLI installed and authenticated
## Environment Variables
Set the following environment variables:
```powershell
# Replace with your Azure OpenAI endpoint
$env:AZURE_OPENAI_ENDPOINT="https://your-openai-resource.openai.azure.com/"
# Optional, defaults to gpt-4o-mini
$env:AZURE_OPENAI_DEPLOYMENT_NAME="gpt-4o-mini"
```
## How It Works
The sample uses a mock search function that demonstrates the RAG pattern:
1. When the user asks a question, the TextSearchProvider intercepts it
2. The search function looks for relevant documents based on the query
3. Retrieved documents are injected into the model's context
4. The AI responds using both its training and the provided context
5. The agent can cite specific source documents in its answers
The mock search function returns pre-defined snippets for demonstration purposes. In a production scenario, you would replace this with actual searches against your knowledge base (e.g., Azure AI Search, vector database, etc.).

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name: AgentWithTextSearchRag
displayName: "Text Search RAG Agent"
description: >
An AI agent that uses TextSearchProvider for retrieval augmented generation (RAG) capabilities.
The agent runs searches against an external knowledge base before each model invocation and
injects the results into the model context. It can answer questions about Contoso Outdoors
policies and products, including return policies, refunds, shipping options, and product care
instructions such as tent maintenance.
metadata:
authors:
- Microsoft Agent Framework Team
tags:
- Azure AI AgentServer
- Microsoft Agent Framework
- Retrieval-Augmented Generation
- RAG
template:
kind: hosted
name: AgentWithTextSearchRag
protocols:
- protocol: responses
version: v1
environment_variables:
- name: AZURE_OPENAI_ENDPOINT
value: ${AZURE_OPENAI_ENDPOINT}
- name: AZURE_OPENAI_DEPLOYMENT_NAME
value: gpt-4o-mini
resources:
- name: "gpt-4o-mini"
kind: model
id: gpt-4o-mini

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@host = http://localhost:8088
@endpoint = {{host}}/responses
### Health Check
GET {{host}}/readiness
### Simple string input
POST {{endpoint}}
Content-Type: application/json
{
"input": "Hi! I need help understanding the return policy."
}
### Explicit input
POST {{endpoint}}
Content-Type: application/json
{
"input": [
{
"type": "message",
"role": "user",
"content": [
{
"type": "input_text",
"text": "How long does standard shipping usually take?"
}
]
}
]
}

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<Project Sdk="Microsoft.NET.Sdk">
<PropertyGroup>
<OutputType>Exe</OutputType>
<TargetFrameworks>net10.0</TargetFrameworks>
<Nullable>enable</Nullable>
<ImplicitUsings>enable</ImplicitUsings>
<!--
Disable central package management for this project.
This project requires explicit package references with versions specified inline rather than
inheriting them from Directory.Packages.props. This is necessary because a Docker image will
be created from this project, and the Docker build process only has access to this folder
and cannot access parent folders where Directory.Packages.props resides.
-->
<ManagePackageVersionsCentrally>false</ManagePackageVersionsCentrally>
</PropertyGroup>
<!--
Remove analyzer PackageReference items inherited from Directory.Packages.props.
Note: ManagePackageVersionsCentrally only controls PackageVersion items, not PackageReference items.
Directory.Packages.props contains both PackageVersion and PackageReference entries for analyzers,
and the PackageReference items are always inherited through MSBuild imports regardless of the
ManagePackageVersionsCentrally setting. We must explicitly remove them before adding our own versions.
-->
<ItemGroup>
<PackageReference Remove="Microsoft.CodeAnalysis.NetAnalyzers" />
<PackageReference Remove="Microsoft.VisualStudio.Threading.Analyzers" />
<PackageReference Remove="xunit.analyzers" />
<PackageReference Remove="Moq.Analyzers" />
<PackageReference Remove="Roslynator.Analyzers" />
<PackageReference Remove="Roslynator.CodeAnalysis.Analyzers" />
<PackageReference Remove="Roslynator.Formatting.Analyzers" />
</ItemGroup>
<ItemGroup>
<PackageReference Include="Azure.AI.AgentServer.AgentFramework" Version="1.0.0-beta.5" />
<PackageReference Include="Azure.AI.OpenAI" Version="2.7.0-beta.2" />
<PackageReference Include="Azure.Identity" Version="1.17.1" />
<PackageReference Include="Microsoft.Agents.AI.Workflows" Version="1.0.0-preview.251219.1" />
<PackageReference Include="Microsoft.Extensions.AI.OpenAI" Version="10.1.0-preview.1.25608.1" />
</ItemGroup>
<!-- Add analyzers with compatible versions -->
<ItemGroup>
<PackageReference Include="Microsoft.CodeAnalysis.NetAnalyzers" Version="10.0.100">
<PrivateAssets>all</PrivateAssets>
<IncludeAssets>runtime; build; native; contentfiles; analyzers; buildtransitive</IncludeAssets>
</PackageReference>
<PackageReference Include="Microsoft.VisualStudio.Threading.Analyzers" Version="17.14.15">
<PrivateAssets>all</PrivateAssets>
<IncludeAssets>runtime; build; native; contentfiles; analyzers; buildtransitive</IncludeAssets>
</PackageReference>
<PackageReference Include="Roslynator.Analyzers" Version="4.14.1">
<PrivateAssets>all</PrivateAssets>
<IncludeAssets>runtime; build; native; contentfiles; analyzers; buildtransitive</IncludeAssets>
</PackageReference>
<PackageReference Include="Roslynator.CodeAnalysis.Analyzers" Version="4.14.1">
<PrivateAssets>all</PrivateAssets>
<IncludeAssets>runtime; build; native; contentfiles; analyzers; buildtransitive</IncludeAssets>
</PackageReference>
<PackageReference Include="Roslynator.Formatting.Analyzers" Version="4.14.1">
<PrivateAssets>all</PrivateAssets>
<IncludeAssets>runtime; build; native; contentfiles; analyzers; buildtransitive</IncludeAssets>
</PackageReference>
</ItemGroup>
</Project>

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@@ -0,0 +1,20 @@
# Build the application
FROM mcr.microsoft.com/dotnet/sdk:10.0-alpine AS build
WORKDIR /src
# Copy files from the current directory on the host to the working directory in the container
COPY . .
RUN dotnet restore
RUN dotnet build -c Release --no-restore
RUN dotnet publish -c Release --no-build -o /app -f net10.0
# Run the application
FROM mcr.microsoft.com/dotnet/aspnet:10.0-alpine AS final
WORKDIR /app
# Copy everything needed to run the app from the "build" stage.
COPY --from=build /app .
EXPOSE 8088
ENTRYPOINT ["dotnet", "AgentsInWorkflows.dll"]

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@@ -0,0 +1,37 @@
// Copyright (c) Microsoft. All rights reserved.
// This sample demonstrates how to integrate AI agents into a workflow pipeline.
// Three translation agents are connected sequentially to create a translation chain:
// English → French → Spanish → English, showing how agents can be composed as workflow executors.
using Azure.AI.AgentServer.AgentFramework.Extensions;
using Azure.AI.OpenAI;
using Azure.Identity;
using Microsoft.Agents.AI;
using Microsoft.Agents.AI.Workflows;
using Microsoft.Extensions.AI;
// Set up the Azure OpenAI client
var endpoint = Environment.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT") ?? throw new InvalidOperationException("AZURE_OPENAI_ENDPOINT is not set.");
var deploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT_NAME") ?? "gpt-4o-mini";
IChatClient chatClient = new AzureOpenAIClient(new Uri(endpoint), new DefaultAzureCredential())
.GetChatClient(deploymentName)
.AsIChatClient();
// Create agents
AIAgent frenchAgent = GetTranslationAgent("French", chatClient);
AIAgent spanishAgent = GetTranslationAgent("Spanish", chatClient);
AIAgent englishAgent = GetTranslationAgent("English", chatClient);
// Build the workflow and turn it into an agent
AIAgent agent = new WorkflowBuilder(frenchAgent)
.AddEdge(frenchAgent, spanishAgent)
.AddEdge(spanishAgent, englishAgent)
.Build()
.AsAgent();
await agent.RunAIAgentAsync();
static ChatClientAgent GetTranslationAgent(string targetLanguage, IChatClient chatClient) =>
new(chatClient, $"You are a translation assistant that translates the provided text to {targetLanguage}.");

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# What this sample demonstrates
This sample demonstrates the use of AI agents as executors within a workflow.
This workflow uses three translation agents:
1. French Agent - translates input text to French
2. Spanish Agent - translates French text to Spanish
3. English Agent - translates Spanish text back to English
The agents are connected sequentially, creating a translation chain that demonstrates how AI-powered components can be seamlessly integrated into workflow pipelines.
## Prerequisites
Before you begin, ensure you have the following prerequisites:
- .NET 10 SDK or later
- Azure OpenAI 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 OpenAI 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_OPENAI_ENDPOINT="https://your-resource.openai.azure.com/" # Replace with your Azure OpenAI resource endpoint
$env:AZURE_OPENAI_DEPLOYMENT_NAME="gpt-4o-mini" # Optional, defaults to gpt-4o-mini

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name: AgentsInWorkflows
displayName: "Translation Chain Workflow Agent"
description: >
A workflow agent that performs sequential translation through multiple languages.
The agent translates text from English to French, then to Spanish, and finally back
to English, leveraging AI-powered translation capabilities in a pipeline workflow.
metadata:
authors:
- Microsoft Agent Framework Team
tags:
- Azure AI AgentServer
- Microsoft Agent Framework
- Workflows
template:
kind: hosted
name: AgentsInWorkflows
protocols:
- protocol: responses
version: v1
environment_variables:
- name: AZURE_OPENAI_ENDPOINT
value: ${AZURE_OPENAI_ENDPOINT}
- name: AZURE_OPENAI_DEPLOYMENT_NAME
value: gpt-4o-mini
resources:
- name: "gpt-4o-mini"
kind: model
id: gpt-4o-mini

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@host = http://localhost:8088
@endpoint = {{host}}/responses
### Health Check
GET {{host}}/readiness
### Simple string input
POST {{endpoint}}
Content-Type: application/json
{
"input": "Hello, how are you today?"
}
### Explicit input
POST {{endpoint}}
Content-Type: application/json
{
"input": [
{
"type": "message",
"role": "user",
"content": [
{
"type": "input_text",
"text": "Hello, how are you today?"
}
]
}
]
}