Skill

Build Persistent AI Agents with .NET SDK

.NET SDK for building persistent Azure AI agents with threads, messages, runs, code interpreter, file search, function calling, Bing grounding, and Azure AI

Works with azurebing

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Updated today
Version 13.1.1

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Why it matters

Develop sophisticated AI agents capable of managing threads, messages, and complex tool integrations. This SDK provides the low-level control needed to build custom agent behaviors for various applications.

Outcomes

What it gets done

01

Create and manage AI agents with CRUD operations.

02

Implement conversational threads and message handling.

03

Integrate tools like code interpreters, file search, and web search.

04

Handle function calls and streaming responses for dynamic agent interaction.

Install

Add it to your toolbox

Run in your project directory:

curl -fsSL https://spark.entire.vc/get/ag-azure-ai-agents-persistent-dotnet | bash

Capabilities

What this skill does

Generate code

Writes source code or scripts from a description.

Chatbot

Handles multi-turn conversations within a defined domain.

RAG index

Chunks, embeds, and indexes documents for semantic retrieval.

Search the web

Searches the web and retrieves relevant sources.

Overview

Azure.AI.Agents.Persistent (.NET)

What it does

Low-level SDK for creating and managing persistent AI agents with threads, messages, runs, and tools

How it connects

When you need to build AI agents with persistent conversation threads, execute Python code, search uploaded documents via vector stores, call custom functions, or integrate Bing web search and Azure AI Search

Source README

Azure.AI.Agents.Persistent (.NET)

Low-level SDK for creating and managing persistent AI agents with threads, messages, runs, and tools.

Installation

dotnet add package Azure.AI.Agents.Persistent --prerelease
dotnet add package Azure.Identity

Current Versions: Stable v1.1.0, Preview v1.2.0-beta.8

Environment Variables

PROJECT_ENDPOINT=https://<resource>.services.ai.azure.com/api/projects/<project>
MODEL_DEPLOYMENT_NAME=gpt-4o-mini
AZURE_BING_CONNECTION_ID=<bing-connection-resource-id>
AZURE_AI_SEARCH_CONNECTION_ID=<search-connection-resource-id>

Authentication

using Azure.AI.Agents.Persistent;
using Azure.Identity;

var projectEndpoint = Environment.GetEnvironmentVariable("PROJECT_ENDPOINT");
PersistentAgentsClient client = new(projectEndpoint, new DefaultAzureCredential());

Client Hierarchy

PersistentAgentsClient
├── Administration  → Agent CRUD operations
├── Threads         → Thread management
├── Messages        → Message operations
├── Runs            → Run execution and streaming
├── Files           → File upload/download
└── VectorStores    → Vector store management

Core Workflow

1. Create Agent

var modelDeploymentName = Environment.GetEnvironmentVariable("MODEL_DEPLOYMENT_NAME");

PersistentAgent agent = await client.Administration.CreateAgentAsync(
    model: modelDeploymentName,
    name: "Math Tutor",
    instructions: "You are a personal math tutor. Write and run code to answer math questions.",
    tools: [new CodeInterpreterToolDefinition()]
);

2. Create Thread and Message

// Create thread
PersistentAgentThread thread = await client.Threads.CreateThreadAsync();

// Create message
await client.Messages.CreateMessageAsync(
    thread.Id,
    MessageRole.User,
    "I need to solve the equation `3x + 11 = 14`. Can you help me?"
);

3. Run Agent (Polling)

// Create run
ThreadRun run = await client.Runs.CreateRunAsync(
    thread.Id,
    agent.Id,
    additionalInstructions: "Please address the user as Jane Doe."
);

// Poll for completion
do
{
    await Task.Delay(TimeSpan.FromMilliseconds(500));
    run = await client.Runs.GetRunAsync(thread.Id, run.Id);
}
while (run.Status == RunStatus.Queued || run.Status == RunStatus.InProgress);

// Retrieve messages
await foreach (PersistentThreadMessage message in client.Messages.GetMessagesAsync(
    threadId: thread.Id, 
    order: ListSortOrder.Ascending))
{
    Console.Write($"{message.Role}: ");
    foreach (MessageContent content in message.ContentItems)
    {
        if (content is MessageTextContent textContent)
            Console.WriteLine(textContent.Text);
    }
}

4. Streaming Response

AsyncCollectionResult<StreamingUpdate> stream = client.Runs.CreateRunStreamingAsync(
    thread.Id, 
    agent.Id
);

await foreach (StreamingUpdate update in stream)
{
    if (update.UpdateKind == StreamingUpdateReason.RunCreated)
    {
        Console.WriteLine("--- Run started! ---");
    }
    else if (update is MessageContentUpdate contentUpdate)
    {
        Console.Write(contentUpdate.Text);
    }
    else if (update.UpdateKind == StreamingUpdateReason.RunCompleted)
    {
        Console.WriteLine("\n--- Run completed! ---");
    }
}

5. Function Calling

// Define function tool
FunctionToolDefinition weatherTool = new(
    name: "getCurrentWeather",
    description: "Gets the current weather at a location.",
    parameters: BinaryData.FromObjectAsJson(new
    {
        Type = "object",
        Properties = new
        {
            Location = new { Type = "string", Description = "City and state, e.g. San Francisco, CA" },
            Unit = new { Type = "string", Enum = new[] { "c", "f" } }
        },
        Required = new[] { "location" }
    }, new JsonSerializerOptions { PropertyNamingPolicy = JsonNamingPolicy.CamelCase })
);

// Create agent with function
PersistentAgent agent = await client.Administration.CreateAgentAsync(
    model: modelDeploymentName,
    name: "Weather Bot",
    instructions: "You are a weather bot.",
    tools: [weatherTool]
);

// Handle function calls during polling
do
{
    await Task.Delay(500);
    run = await client.Runs.GetRunAsync(thread.Id, run.Id);

    if (run.Status == RunStatus.RequiresAction 
        && run.RequiredAction is SubmitToolOutputsAction submitAction)
    {
        List<ToolOutput> outputs = [];
        foreach (RequiredToolCall toolCall in submitAction.ToolCalls)
        {
            if (toolCall is RequiredFunctionToolCall funcCall)
            {
                // Execute function and get result
                string result = ExecuteFunction(funcCall.Name, funcCall.Arguments);
                outputs.Add(new ToolOutput(toolCall, result));
            }
        }
        run = await client.Runs.SubmitToolOutputsToRunAsync(run, outputs, toolApprovals: null);
    }
}
while (run.Status == RunStatus.Queued || run.Status == RunStatus.InProgress);

6. File Search with Vector Store

// Upload file
PersistentAgentFileInfo file = await client.Files.UploadFileAsync(
    filePath: "document.txt",
    purpose: PersistentAgentFilePurpose.Agents
);

// Create vector store
PersistentAgentsVectorStore vectorStore = await client.VectorStores.CreateVectorStoreAsync(
    fileIds: [file.Id],
    name: "my_vector_store"
);

// Create file search resource
FileSearchToolResource fileSearchResource = new();
fileSearchResource.VectorStoreIds.Add(vectorStore.Id);

// Create agent with file search
PersistentAgent agent = await client.Administration.CreateAgentAsync(
    model: modelDeploymentName,
    name: "Document Assistant",
    instructions: "You help users find information in documents.",
    tools: [new FileSearchToolDefinition()],
    toolResources: new ToolResources { FileSearch = fileSearchResource }
);

7. Bing Grounding

var bingConnectionId = Environment.GetEnvironmentVariable("AZURE_BING_CONNECTION_ID");

BingGroundingToolDefinition bingTool = new(
    new BingGroundingSearchToolParameters(
        [new BingGroundingSearchConfiguration(bingConnectionId)]
    )
);

PersistentAgent agent = await client.Administration.CreateAgentAsync(
    model: modelDeploymentName,
    name: "Search Agent",
    instructions: "Use Bing to answer questions about current events.",
    tools: [bingTool]
);

8. Azure AI Search

AzureAISearchToolResource searchResource = new(
    connectionId: searchConnectionId,
    indexName: "my_index",
    topK: 5,
    filter: "category eq 'documentation'",
    queryType: AzureAISearchQueryType.Simple
);

PersistentAgent agent = await client.Administration.CreateAgentAsync(
    model: modelDeploymentName,
    name: "Search Agent",
    instructions: "Search the documentation index to answer questions.",
    tools: [new AzureAISearchToolDefinition()],
    toolResources: new ToolResources { AzureAISearch = searchResource }
);

9. Cleanup

await client.Threads.DeleteThreadAsync(thread.Id);
await client.Administration.DeleteAgentAsync(agent.Id);
await client.VectorStores.DeleteVectorStoreAsync(vectorStore.Id);
await client.Files.DeleteFileAsync(file.Id);

Available Tools

Tool Class Purpose
Code Interpreter CodeInterpreterToolDefinition Execute Python code, generate visualizations
File Search FileSearchToolDefinition Search uploaded files via vector stores
Function Calling FunctionToolDefinition Call custom functions
Bing Grounding BingGroundingToolDefinition Web search via Bing
Azure AI Search AzureAISearchToolDefinition Search Azure AI Search indexes
OpenAPI OpenApiToolDefinition Call external APIs via OpenAPI spec
Azure Functions AzureFunctionToolDefinition Invoke Azure Functions
MCP MCPToolDefinition Model Context Protocol tools
SharePoint SharepointToolDefinition Access SharePoint content
Microsoft Fabric MicrosoftFabricToolDefinition Access Fabric data

Streaming Update Types

Update Type Description
StreamingUpdateReason.RunCreated Run started
StreamingUpdateReason.RunInProgress Run processing
StreamingUpdateReason.RunCompleted Run finished
StreamingUpdateReason.RunFailed Run errored
MessageContentUpdate Text content chunk
RunStepUpdate Step status change

Key Types Reference

Type Purpose
PersistentAgentsClient Main entry point
PersistentAgent Agent with model, instructions, tools
PersistentAgentThread Conversation thread
PersistentThreadMessage Message in thread
ThreadRun Execution of agent against thread
RunStatus Queued, InProgress, RequiresAction, Completed, Failed
ToolResources Combined tool resources
ToolOutput Function call response

Best Practices

  1. Always dispose clients - Use using statements or explicit disposal
  2. Poll with appropriate delays - 500ms recommended between status checks
  3. Clean up resources - Delete threads and agents when done
  4. Handle all run statuses - Check for RequiresAction, Failed, Cancelled
  5. Use streaming for real-time UX - Better user experience than polling
  6. Store IDs not objects - Reference agents/threads by ID
  7. Use async methods - All operations should be async

Error Handling

using Azure;

try
{
    var agent = await client.Administration.CreateAgentAsync(...);
}
catch (RequestFailedException ex) when (ex.Status == 404)
{
    Console.WriteLine("Resource not found");
}
catch (RequestFailedException ex)
{
    Console.WriteLine($"Error: {ex.Status} - {ex.ErrorCode}: {ex.Message}");
}

Related SDKs

SDK Purpose Install
Azure.AI.Agents.Persistent Low-level agents (this SDK) dotnet add package Azure.AI.Agents.Persistent
Azure.AI.Projects High-level project client dotnet add package Azure.AI.Projects

Reference Links

Resource URL
NuGet Package https://www.nuget.org/packages/Azure.AI.Agents.Persistent
API Reference https://learn.microsoft.com/dotnet/api/azure.ai.agents.persistent
GitHub Source https://github.com/Azure/azure-sdk-for-net/tree/main/sdk/ai/Azure.AI.Agents.Persistent
Samples https://github.com/Azure/azure-sdk-for-net/tree/main/sdk/ai/Azure.AI.Agents.Persistent/samples

When to Use

This skill is applicable to execute the workflow or actions described in the overview.

Limitations

  • Use this skill only when the task clearly matches the scope described above.
  • Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
  • Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.

Discussion

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