.github/plugins/azure-sdk-dotnet/skills/azure-ai-agents-persistent-dotnet/SKILL.md
Azure AI Agents Persistent SDK for .NET. Low-level SDK for creating and managing AI agents with threads, messages, runs, and tools. Use for agent CRUD, conversation threads, streaming responses, function calling, file search, and code interpreter. Triggers: "PersistentAgentsClient", "persistent agents", "agent threads", "agent runs", "streaming agents", "function calling agents .NET".
npx skillsauth add microsoft/skills azure-ai-agents-persistent-dotnetInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Low-level SDK for creating and managing persistent AI agents with threads, messages, runs, and tools.
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
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>
using Azure.AI.Agents.Persistent;
using Azure.Identity;
var projectEndpoint = Environment.GetEnvironmentVariable("PROJECT_ENDPOINT");
PersistentAgentsClient client = new(projectEndpoint, new DefaultAzureCredential());
PersistentAgentsClient
├── Administration → Agent CRUD operations
├── Threads → Thread management
├── Messages → Message operations
├── Runs → Run execution and streaming
├── Files → File upload/download
└── VectorStores → Vector store management
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()]
);
// 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?"
);
// 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);
}
}
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! ---");
}
}
// 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);
// 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 }
);
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]
);
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 }
);
await client.Threads.DeleteThreadAsync(thread.Id);
await client.Administration.DeleteAgentAsync(agent.Id);
await client.VectorStores.DeleteVectorStoreAsync(vectorStore.Id);
await client.Files.DeleteFileAsync(file.Id);
| 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 |
| 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 |
| 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 |
using statements or explicit disposalRequiresAction, Failed, Cancelledusing 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}");
}
| 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 |
| 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 |
tools
KQL language expertise for writing correct, efficient Kusto Query Language queries. Covers syntax gotchas, join patterns, dynamic types, datetime pitfalls, regex patterns, serialization, memory management, result-size discipline, and advanced functions (geo, vector, graph). USE THIS SKILL whenever writing, debugging, or reviewing KQL queries — even simple ones — because the gotchas section prevents the most common errors that waste tool calls and cause expensive retry cascades. Trigger on: KQL, Kusto, ADX, Azure Data Explorer, Fabric Real-Time Intelligence, EventHouse, Log Analytics, log analysis, data exploration, time series, anomaly detection, summarize, where clause, join, extend, project, let statement, parse operator, extract function, any mention of pipe-forward query syntax.
development
Deploy, evaluate, and manage Foundry agents end-to-end: Docker build, ACR push, hosted/prompt agent create, container start, batch eval, prompt optimization, prompt optimizer workflows, agent.yaml, dataset curation from traces. USE FOR: deploy agent to Foundry, hosted agent, create agent, invoke agent, evaluate agent, run batch eval, optimize prompt, improve prompt, prompt optimization, prompt optimizer, improve agent instructions, optimize agent instructions, optimize system prompt, deploy model, Foundry project, RBAC, role assignment, permissions, quota, capacity, region, troubleshoot agent, deployment failure, create dataset from traces, dataset versioning, eval trending, create AI Services, Cognitive Services, create Foundry resource, provision resource, knowledge index, agent monitoring, customize deployment, onboard, availability. DO NOT USE FOR: Azure Functions, App Service, general Azure deploy (use azure-deploy), general Azure prep (use azure-prepare).
testing
Pre-deployment validation for Azure readiness. Run deep checks on configuration, infrastructure (Bicep or Terraform), RBAC role assignments, managed identity permissions, and prerequisites before deploying. WHEN: validate my app, check deployment readiness, run preflight checks, verify configuration, check if ready to deploy, validate azure.yaml, validate Bicep, test before deploying, troubleshoot deployment errors, validate Azure Functions, validate function app, validate serverless deployment, verify RBAC roles, check role assignments, review managed identity permissions, what-if analysis, validate Container Apps deployment.
testing
Check/manage Azure quotas and usage across providers. For deployment planning, capacity validation, region selection. WHEN: "check quotas", "service limits", "current usage", "request quota increase", "quota exceeded", "validate capacity", "regional availability", "provisioning limits", "vCPU limit", "how many vCPUs available in my subscription".