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This commit is contained in:
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// Copyright (c) Microsoft. All rights reserved.
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using System.Diagnostics;
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using Azure.AI.Projects;
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using Azure.Identity;
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using Azure.Monitor.OpenTelemetry.Exporter;
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using Microsoft.Agents.AI;
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using Microsoft.Agents.AI.Workflows;
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using Microsoft.Extensions.AI;
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using OpenTelemetry;
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using OpenTelemetry.Resources;
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using OpenTelemetry.Trace;
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namespace WorkflowAsAnAgentObservabilitySample;
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/// <summary>
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/// This sample shows how to enable OpenTelemetry observability for workflows when
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/// using them as <see cref="AIAgent"/>s.
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///
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/// In this example, we create a workflow that uses two language agents to process
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/// input concurrently, one that responds in French and another that responds in English.
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///
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/// You will interact with the workflow in an interactive loop, sending messages and receiving
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/// streaming responses from the workflow as if it were an agent who responds in both languages.
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///
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/// OpenTelemetry observability is enabled at multiple levels:
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/// 1. At the chat client level, capturing telemetry for interactions with the Azure OpenAI service.
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/// 2. At the agent level, capturing telemetry for agent operations.
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/// 3. At the workflow level, capturing telemetry for workflow execution.
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///
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/// Traces will be sent to an Aspire dashboard via an OTLP endpoint, and optionally to
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/// Azure Monitor if an Application Insights connection string is provided.
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///
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/// Learn how to set up an Aspire dashboard here:
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/// https://learn.microsoft.com/en-us/dotnet/aspire/fundamentals/dashboard/standalone?tabs=bash
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/// </summary>
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/// <remarks>
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/// Pre-requisites:
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/// - Foundational samples should be completed first.
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/// - This sample uses concurrent processing.
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/// - An Azure OpenAI endpoint and deployment name.
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/// - An Application Insights resource for telemetry (optional).
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/// </remarks>
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public static class Program
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{
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private const string SourceName = "Workflow.ApplicationInsightsSample";
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private static readonly ActivitySource s_activitySource = new(SourceName);
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private static async Task Main()
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{
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// Set up observability
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var applicationInsightsConnectionString = Environment.GetEnvironmentVariable("APPLICATIONINSIGHTS_CONNECTION_STRING");
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var otlpEndpoint = Environment.GetEnvironmentVariable("OTEL_EXPORTER_OTLP_ENDPOINT") ?? "http://localhost:4317";
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var resourceBuilder = ResourceBuilder
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.CreateDefault()
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.AddService("WorkflowSample");
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var traceProviderBuilder = Sdk.CreateTracerProviderBuilder()
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.SetResourceBuilder(resourceBuilder)
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.AddSource("Microsoft.Agents.AI.*") // Agent Framework telemetry
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.AddSource("Microsoft.Extensions.AI.*") // Extensions AI telemetry
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.AddSource(SourceName);
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traceProviderBuilder.AddOtlpExporter(options => options.Endpoint = new Uri(otlpEndpoint));
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if (!string.IsNullOrWhiteSpace(applicationInsightsConnectionString))
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{
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traceProviderBuilder.AddAzureMonitorTraceExporter(options => options.ConnectionString = applicationInsightsConnectionString);
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}
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using var traceProvider = traceProviderBuilder.Build();
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// Set up the Azure AI Foundry client
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var endpoint = Environment.GetEnvironmentVariable("FOUNDRY_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("FOUNDRY_PROJECT_ENDPOINT is not set.");
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var deploymentName = Environment.GetEnvironmentVariable("FOUNDRY_MODEL") ?? "gpt-5.4-mini";
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AIProjectClient aiProjectClient = new(new Uri(endpoint), new DefaultAzureCredential());
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// Start a root activity for the application
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using var activity = s_activitySource.StartActivity("main");
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Console.WriteLine($"Operation/Trace ID: {Activity.Current?.TraceId}");
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// Create the workflow and turn it into an agent with OpenTelemetry instrumentation
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var workflow = WorkflowHelper.GetWorkflow(aiProjectClient, deploymentName, SourceName);
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var agent = new OpenTelemetryAgent(workflow.AsAIAgent("workflow-agent", "Workflow Agent"), SourceName)
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{
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EnableSensitiveData = true // enable sensitive data at the agent level such as prompts and responses
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};
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var session = await agent.CreateSessionAsync();
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// Start an interactive loop to interact with the workflow as if it were an agent
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while (true)
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{
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Console.WriteLine();
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Console.Write("User (or 'exit' to quit): ");
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string? input = Console.ReadLine();
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if (string.IsNullOrWhiteSpace(input) || input.Equals("exit", StringComparison.OrdinalIgnoreCase))
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{
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break;
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}
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await ProcessInputAsync(agent, session, input);
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}
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// Helper method to process user input and display streaming responses. To display
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// multiple interleaved responses correctly, we buffer updates by message ID and
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// re-render all messages on each update.
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static async Task ProcessInputAsync(AIAgent agent, AgentSession? session, string input)
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{
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Dictionary<string, List<AgentResponseUpdate>> buffer = [];
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await foreach (AgentResponseUpdate update in agent.RunStreamingAsync(input, session))
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{
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if (update.MessageId is null || string.IsNullOrEmpty(update.Text))
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{
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// skip updates that don't have a message ID or text
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continue;
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}
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Console.Clear();
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if (!buffer.TryGetValue(update.MessageId, out List<AgentResponseUpdate>? value))
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{
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value = [];
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buffer[update.MessageId] = value;
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}
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value.Add(update);
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foreach (var (messageId, segments) in buffer)
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{
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string combinedText = string.Concat(segments);
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Console.WriteLine($"{segments[0].AuthorName}: {combinedText}");
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Console.WriteLine();
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}
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}
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}
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}
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}
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