Azure.AI.Agents.Persistent (.NET)

Overview

This public intake copy packages

plugins/antigravity-awesome-skills-claude/skills/azure-ai-agents-persistent-dotnet

https://github.com/sickn33/antigravity-awesome-skills

Use it when the operator needs the upstream workflow, support files, and repository context to stay intact while the public validator and private enhancer continue their normal downstream flow.

This intake keeps the copied upstream files intact and uses

metadata.json

ORIGIN.md

Azure.AI.Agents.Persistent (.NET) Low-level SDK for creating and managing persistent AI agents with threads, messages, runs, and tools.

Imported source sections that did not map cleanly to the public headings are still preserved below or in the support files. Notable imported sections: Environment Variables, Authentication, Client Hierarchy, Available Tools, Streaming Update Types, Error Handling.

When to Use This Skill

Use this section as the trigger filter. It should make the activation boundary explicit before the operator loads files, runs commands, or opens a pull request.

• This skill is applicable to execute the workflow or actions described in the overview.
• Use when the request clearly matches the imported source intent: Azure AI Agents Persistent SDK for .NET. Low-level SDK for creating and managing AI agents with threads, messages, runs, and tools.
• Use when the operator should preserve upstream workflow detail instead of rewriting the process from scratch.
• Use when provenance needs to stay visible in the answer, PR, or review packet.
• Use when copied upstream references, examples, or scripts materially improve the answer.
• Use when the workflow should remain reviewable in the public intake repo before the private enhancer takes over.

Operating Table

metadata.json

ORIGIN.md

SKILL.md

SKILL.md

## Related Skills

Workflow

This workflow is intentionally editorial and operational at the same time. It keeps the imported source useful to the operator while still satisfying the public intake standards that feed the downstream enhancer flow.

1. `bash 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 ### 1.
2. Create Agent `csharp var modelDeploymentName = Environment.GetEnvironmentVariable("MODELDEPLOYMENTNAME"); PersistentAgent agent = await client.Administration.CreateAgentAsync( model: modelDeploymentName, name: "Math Tutor", instructions: "You are a personal math tutor.
3. Write and run code to answer math questions.", tools: [new CodeInterpreterToolDefinition()] ); ` ### 2.
4. Create Thread and Message `csharp // 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.
5. Can you help me?" ); ` ### 3.
6. Run Agent (Polling) csharp // 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.
7. Streaming Response `csharp 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!

Imported Workflow Notes

Imported: 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

Imported: 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);

Imported: 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>

Examples

Example 1: Ask for the upstream workflow directly

Use @azure-ai-agents-persistent-dotnet to handle <task>. Start from the copied upstream workflow, load only the files that change the outcome, and keep provenance visible in the answer.

Explanation: This is the safest starting point when the operator needs the imported workflow, but not the entire repository.

Example 2: Ask for a provenance-grounded review

Review @azure-ai-agents-persistent-dotnet against metadata.json and ORIGIN.md, then explain which copied upstream files you would load first and why.

Explanation: Use this before review or troubleshooting when you need a precise, auditable explanation of origin and file selection.

Example 3: Narrow the copied support files before execution

Use @azure-ai-agents-persistent-dotnet for <task>. Load only the copied references, examples, or scripts that change the outcome, and name the files explicitly before proceeding.

Explanation: This keeps the skill aligned with progressive disclosure instead of loading the whole copied package by default.

Example 4: Build a reviewer packet

Review @azure-ai-agents-persistent-dotnet using the copied upstream files plus provenance, then summarize any gaps before merge.

Explanation: This is useful when the PR is waiting for human review and you want a repeatable audit packet.

Best Practices

Treat the generated public skill as a reviewable packaging layer around the upstream repository. The goal is to keep provenance explicit and load only the copied source material that materially improves execution.

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

Imported Operating Notes

Imported: 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

Troubleshooting

Problem: The operator skipped the imported context and answered too generically

Symptoms: The result ignores the upstream workflow in

plugins/antigravity-awesome-skills-claude/skills/azure-ai-agents-persistent-dotnet

metadata.json

ORIGIN.md

Problem: The imported workflow feels incomplete during review

Symptoms: Reviewers can see the generated

SKILL.md

Problem: The task drifted into a different specialization

Symptoms: The imported skill starts in the right place, but the work turns into debugging, architecture, design, security, or release orchestration that a native skill handles better. Solution: Use the related skills section to hand off deliberately. Keep the imported provenance visible so the next skill inherits the right context instead of starting blind.

Related Skills

• @ai-dev-jobs-mcp

  - Use when the work is better handled by that native specialization after this imported skill establishes context.

• @arm-cortex-expert

  - Use when the work is better handled by that native specialization after this imported skill establishes context.

• @asana-automation

  - Use when the work is better handled by that native specialization after this imported skill establishes context.

• @ask-questions-if-underspecified

  - Use when the work is better handled by that native specialization after this imported skill establishes context.

Additional Resources

Use this support matrix and the linked files below as the operator packet for this imported skill. They should reflect real copied source material, not generic scaffolding.

references

references/n/a

examples

examples/n/a

scripts

scripts/n/a

agents

agents/n/a

assets

assets/n/a

Imported Reference Notes

Imported: Key Types Reference

PersistentAgentsClient

PersistentAgent

PersistentAgentThread

PersistentThreadMessage

ThreadRun

RunStatus

ToolResources

ToolOutput

Imported: Reference Links

Imported: Authentication

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

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

Imported: 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

Imported: Available Tools

CodeInterpreterToolDefinition

FileSearchToolDefinition

FunctionToolDefinition

BingGroundingToolDefinition

AzureAISearchToolDefinition

OpenApiToolDefinition

AzureFunctionToolDefinition

MCPToolDefinition

SharepointToolDefinition

MicrosoftFabricToolDefinition

Imported: Streaming Update Types

StreamingUpdateReason.RunCreated

StreamingUpdateReason.RunInProgress

StreamingUpdateReason.RunCompleted

StreamingUpdateReason.RunFailed

MessageContentUpdate

RunStepUpdate

Imported: 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}");
}

Imported: 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.