> ## Documentation Index
> Fetch the complete documentation index at: https://docs.superline.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Detection Result

> API reference for the DetectionResult object

# DetectionResult

The `DetectionResult` object contains the outcome of the agent detection process. It's returned by the `AgentDetector.finalizeDetection()` and `AgentDetector.getCurrentDetectionResult()` methods and provides information about whether the current session is classified as an AI agent.

## Properties

<ResponseField name="isAgent" type="boolean" required>
  Indicates whether the current session is classified as an AI agent based on the default threshold (0.5).

  * `true`: The session is classified as an AI agent (confidence >= 0.5)
  * `false`: The session is classified as a human user (confidence \< 0.5)
</ResponseField>

<ResponseField name="confidence" type="number" required>
  The predicted probability/confidence from the detection model, ranging from 0 to 1.

  * Higher values (closer to 1) indicate higher likelihood of being an AI agent
  * Lower values (closer to 0) indicate higher likelihood of being a human user

  This value represents the model's calculated probability that the session is from an AI agent.
</ResponseField>

<ResponseField name="features" type="Record<string, any>" optional>
  The combined, preprocessed features used for scoring.

  This property contains the feature values *after* preprocessing (standardization, one-hot encoding, etc.) that were fed into the model.
  It can be useful for debugging or understanding the model's decision, especially when `debug: true` is enabled in the `init` options.
</ResponseField>

## Example Result

```javascript theme={null}
// Example detection result
{
  isAgent: true,
  confidence: 0.87,
  // features object contains preprocessed values
  features: {
    mouse_move_speed_mean: 1.23, // Standardized value
    has_touch_screen: 0, // Boolean converted to number
    browser_name_chrome: 1, // One-hot encoded category
    browser_name_firefox: 0,
    // ... other preprocessed features
  }
}
```

## Using the Result

<CodeGroup>
  ```javascript Basic Usage theme={null}
  // detector instance created and initialized
  AgentDetector.init();

  // Later, get the detection result
  async function checkAgent() {
    const result = await AgentDetector.finalizeDetection();
    
    if (result.isAgent) {
      console.log('AI agent detected with confidence:', result.confidence);
      // Handle agent scenario
    } else {
      console.log('Human user detected with confidence:', result.confidence);
      // Handle human scenario
    }
  }
  ```

  ```javascript With Analytics theme={null}
  // detector instance created and initialized
  AgentDetector.init();

  // Later, integrate with analytics
  async function trackUserType() {
    const result = await AgentDetector.finalizeDetection();
    
    // Send to Google Analytics
    gtag('set', 'user_properties', {
      is_agent: result.isAgent,
      agent_confidence: result.confidence
    });
    
    // Or send to Mixpanel
    mixpanel.people.set({
      'Is Agent': result.isAgent,
      'Agent Confidence': result.confidence
      // Confidence is not available in the result object
    });
  }
  ```

  ```javascript Debug Features theme={null}
  // detector instance created and initialized with debug: true
  AgentDetector.init({ debug: true });

  // Later, analyze features
  async function analyzeFeatures() {
    const result = await AgentDetector.finalizeDetection();
    
    console.log('Is agent:', result.isAgent);
    console.log('Confidence:', result.confidence);
    
    // Access and analyze preprocessed features
    if (result.features) {
      console.log('Preprocessed Features:', result.features);
      
      // Example: Log specific feature values
      console.log('Mouse Speed Mean (Standardized):', result.features.mouse_move_speed_mean);
      console.log('Browser (Chrome - OHE):', result.features.browser_name_chrome);
    }
  }
  ```
</CodeGroup>

## Interpretation Guidelines

When interpreting detection results, consider the following guidelines:

<Accordion title="Confidence Thresholds">
  * **confidence \< 0.3**: Strong indication of human user
  * **0.3 ≤ confidence \< 0.5**: Likely human user
  * **0.5 ≤ confidence \< 0.7**: Uncertain, could be either human or agent
  * **0.7 ≤ confidence \< 0.9**: Likely AI agent
  * **confidence ≥ 0.9**: Strong indication of AI agent

  The library uses a default classification threshold of 0.5 to set the `isAgent` boolean. You might adjust your application's response based on the `confidence` value for more nuanced handling.
</Accordion>

<Accordion title="Edge Cases">
  Some scenarios may lead to less reliable detection:

  * Very short sessions with limited interaction (less data for extractors)
  * Users with assistive technologies that modify input patterns
  * Unusual input devices (e.g., trackballs, graphics tablets) or browser configurations
  * High network latency affecting event timing

  Consider these factors when making critical decisions based on detection results.
</Accordion>

## Next Steps

<CardGroup cols={2}>
  <Card title="Agent Detector" icon="magnifying-glass" href="/api-reference/agent-detector">
    Explore the main Agent Detector API
  </Card>

  <Card title="Configuration" icon="gear" href="/api-reference/configuration">
    Learn about configuration options for the `init` method
  </Card>
</CardGroup>