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Examples.NETAutoGen Multi-Agent

AutoGen Multi-Agent Example

This example builds a two-agent loan approval workflow — an Orchestrator that coordinates the process and a LoanAnalyst that evaluates applications. All agent-to-model calls are traced automatically via AutoGenOTelAdapter.

Packages used

  • Veriproof.Sdk.Core — OTel exporter
  • Veriproof.Sdk.Annotations — governance attributes
  • Veriproof.Sdk.Instrumentation.AutoGen — AutoGenOTelAdapter, AgentRunHandle
  • Microsoft.Extensions.AI — IChatClient abstraction

Prerequisites

dotnet add package Veriproof.Sdk.Core
dotnet add package Veriproof.Sdk.Annotations
dotnet add package Veriproof.Sdk.Instrumentation.AutoGen
dotnet add package Microsoft.Extensions.AI
dotnet add package Microsoft.Extensions.AI.OpenAI

Configure DI

// Program.cs
using Microsoft.Extensions.AI;
using OpenTelemetry.Trace;
using Veriproof.Sdk;
using Veriproof.Sdk.Instrumentation.AutoGen;
 
var builder = WebApplication.CreateBuilder(args);
 
// 1. Register base chat client (model-level)
builder.Services.AddSingleton<IChatClient>(_ =>
    new OpenAI.OpenAIClient(Environment.GetEnvironmentVariable("OPENAI_API_KEY")!)
        .AsChatClient("gpt-4.1")
);
 
// 2. Register AutoGen adapter
builder.Services.AddVeriproofAutoGenInstrumentation(options =>
{
    options.GenAiSystem           = "autogen";
    options.CaptureMessageContent = false;
});
 
// 3. Configure VeriProof OTel exporter
builder.Services.AddOpenTelemetry()
    .WithTraces(traces => traces
        .AddSource("*")
        .AddVeriproofTracing(options =>
        {
            options.ApiKey        = Environment.GetEnvironmentVariable("VERIPROOF_API_KEY")!;
            options.ApplicationId = Environment.GetEnvironmentVariable("VERIPROOF_APP_ID")!;
        })
    );
 
builder.Services.AddSingleton<LoanApprovalWorkflow>();

Workflow implementation

// LoanApprovalWorkflow.cs
using System.Diagnostics;
using Microsoft.Extensions.AI;
using Veriproof.Sdk.Annotations;
using Veriproof.Sdk.Annotations.Models;
using Veriproof.Sdk.Instrumentation.AutoGen;
 
public sealed class LoanApprovalWorkflow(
    AutoGenOTelAdapter adapter,
    IChatClient        chatClient,
    ILogger<LoanApprovalWorkflow> logger)
{
    public async Task<LoanDecision> EvaluateAsync(
        string           applicationId,
        string           applicantId,
        int              creditScore,
        decimal          requestedAmount,
        CancellationToken ct = default)
    {
        // ── Open agent_run span — covers the whole workflow ──────────────────
        using var run = adapter.StartAgentRun(
            workflowName: "LoanApprovalWorkflow",
            agentName:    "Orchestrator",
            sessionId:    applicantId
        );
 
        run.SetTag("veriproof.transaction.id",  $"loan:{applicationId}")
           .SetTag("loan.application.id",        applicationId)
           .SetTag("loan.applicant.id",           applicantId)
           .SetTag("loan.credit_score",           creditScore.ToString())
           .SetTag("loan.requested_amount",       requestedAmount.ToString("F2"));
 
        try
        {
            // ── Agent 1: LoanAnalyst ─────────────────────────────────────────
            var analystClient = adapter.WrapClient(
                inner:          chatClient,
                agentName:      "LoanAnalyst",
                defaultModelId: "gpt-4.1"
            );
 
            var analystMessages = new ChatMessage[]
            {
                new(ChatRole.System,
                    "You are an expert loan analyst. Given the applicant's credit profile, " +
                    "return a JSON object with: " +
                    "{ \"riskTier\": \"LOW|MEDIUM|HIGH\", \"recommendation\": \"APPROVE|CONDITIONAL|DENY\", " +
                    "\"confidenceScore\": 0.0-1.0, \"justification\": \"...\" }"),
                new(ChatRole.User,
                    $"Application ID: {applicationId}. " +
                    $"Applicant: {applicantId}. " +
                    $"Credit score: {creditScore}. " +
                    $"Requested amount: {requestedAmount:C0}.")
            };
 
            var analystResponse = await analystClient.GetResponseAsync(analystMessages, ct: ct);
            var analystOutput   = analystResponse.Message.Text ?? "{}";
 
            logger.LogInformation("LoanAnalyst response: {Output}", analystOutput);
 
            // ── Agent 2: ComplianceReviewer ──────────────────────────────────
            var reviewerClient = adapter.WrapClient(
                inner:          chatClient,
                agentName:      "ComplianceReviewer",
                defaultModelId: "gpt-4.1"
            );
 
            var reviewMessages = new ChatMessage[]
            {
                new(ChatRole.System,
                    "You are a compliance officer. Review the analyst's recommendation against " +
                    "regulatory requirements (ECOA, Fair Lending). " +
                    "Return JSON: { \"compliant\": true|false, \"finalDecision\": \"APPROVE|DENY\", " +
                    "\"complianceNotes\": \"...\" }"),
                new(ChatRole.User,
                    $"Analyst output: {analystOutput}")
            };
 
            var reviewResponse = await reviewerClient.GetResponseAsync(reviewMessages, ct: ct);
            var reviewOutput   = reviewResponse.Message.Text ?? "{}";
 
            logger.LogInformation("ComplianceReviewer response: {Output}", reviewOutput);
 
            // ── Parse decision ───────────────────────────────────────────────
            var approved = reviewOutput.Contains("\"APPROVE\"",
                StringComparison.OrdinalIgnoreCase);
 
            var riskLevel = analystOutput.Contains("LOW",    StringComparison.OrdinalIgnoreCase) ? RiskLevel.Low
                          : analystOutput.Contains("MEDIUM", StringComparison.OrdinalIgnoreCase) ? RiskLevel.Medium
                          : RiskLevel.High;
 
            // ── Enrich the agent_run span with governance outcome ──────────
            run.SetTag("veriproof.session.intent",        "transaction")
               .SetTag("veriproof.session.outcome",       approved ? "success" : "failure")
               .SetTag("veriproof.risk.level",            riskLevel.ToString().ToLowerInvariant())
               .SetTag("veriproof.governance.decision",   approved ? "APPROVE" : "DENY")
               .SetTag("loan.compliance.reviewed",        "true");
 
            if (riskLevel >= RiskLevel.Medium)
            {
                // Add a typed risk factor via Activity extension
                Activity.Current?
                    .AddRiskFactor(RiskFactor.LowConfidence(
                        $"Credit score {creditScore} in medium-risk range"));
            }
 
            return new LoanDecision(
                ApplicationId: applicationId,
                Approved:      approved,
                RiskLevel:     riskLevel,
                AnalystOutput: analystOutput,
                ReviewOutput:  reviewOutput
            );
        }
        catch (Exception ex)
        {
            run.SetFailed(ex);
            throw;
        }
    }
}
 
public record LoanDecision(
    string    ApplicationId,
    bool      Approved,
    RiskLevel RiskLevel,
    string    AnalystOutput,
    string    ReviewOutput
);

Span hierarchy

agent_run (LoanApprovalWorkflow / Orchestrator)
│  veriproof.transaction.id      = "loan:APP-001"
│  veriproof.session.outcome     = "success"
│  veriproof.risk.level          = "medium"
│  veriproof.governance.decision = "APPROVE"
│
├── chat_completion
│     gen_ai.agent.name       = "LoanAnalyst"
│     gen_ai.request.model    = "gpt-4.1"
│     gen_ai.usage.input_tokens  = 287
│     gen_ai.usage.output_tokens = 94
│
└── chat_completion
      gen_ai.agent.name       = "ComplianceReviewer"
      gen_ai.request.model    = "gpt-4.1"
      gen_ai.usage.input_tokens  = 193
      gen_ai.usage.output_tokens = 61

AgentRunHandle quick reference

MethodDescription
SetTag(key, value)Add an OTel attribute to the agent_run span; returns this
SetFailed(Exception)Mark span as failed; sets error.type and records the exception
TraceIdW3C-format trace ID — use to correlate with logs
SpanIdSpan ID of the agent_run span
Dispose()End the span (called automatically at end of using block)

Next steps

Last updated on
Governance Session BuilderAPI Keys