Creating Custom Debug Visualizers for Complex AI States

You will develop custom debug drawing functions to visualize internal AI agent data, such as threat levels or target selection, directly in the viewport.

In this lesson, you will learn how to create bespoke, in-viewport debug visualizers to expose the hidden internal decision-making processes of your AI agents.

While the standard Visual Logger is excellent for history, sometimes you need to see exactly what an AI is "thinking" right now—like a dynamically shifting threat level or the specific logic behind why a target was selected. Instead of relying on generic logs, we create custom drawing functions that map internal data to physical shapes in the 3D world.

Implementing a Draw-Debug Function

The most effective way to visualize state is to override the DrawDebug logic within your AI Controller or Character class. By using the DrawDebug functions provided by DrawDebugHelpers.h, you can represent abstract variables as visual cues.

For example, imagine you want to visualize an AI's "Threat Level" relative to a target. You can draw a line from the agent to the target, with the color changing from green (safe) to red (lethal) based on the calculated threat value:

void AMyAIController::VisualizeThreat(AActor* TargetActor, float ThreatLevel)
{
    FColor LineColor = FColor::MakeRedToGreenColorFromScalar(1.0f - ThreatLevel);
    
    DrawDebugLine(
        GetWorld(),
        GetPawn()->GetActorLocation(),
        TargetActor->GetActorLocation(),
        LineColor,
        false, -1.0f, 0, 5.0f
    );

    DrawDebugString(
        GetWorld(), 
        GetPawn()->GetActorLocation() + FVector(0, 0, 100), 
        FString::Printf(TEXT("Threat: %.2f"), ThreatLevel), 
        nullptr, FColor::White, 0.0f
    );
}

Contextual Visualization

When dealing with complex states, avoid "debug clutter." Only trigger these functions when an AI enters a specific state (e.g., CombatState) or when a specific console command is enabled. By drawing spheres at the AI's feet to represent its current "Focus" or using cones to represent its "Target Acquisition Range," you can instantly diagnose why an AI failed to engage a target without ever opening a code file. These visual cues serve as a "live HUD" for your AI's brain, allowing you to catch logic bugs—like an AI stuck in a transition loop—in seconds rather than hours.

Key Takeaway: By mapping internal AI variables to primitive geometric shapes in the viewport, you turn abstract decision-making data into immediate, actionable visual feedback.