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Agent Detection Overview

Superline Agent Detection provides real-time detection of AI agents in browser sessions using metadata and behavioral patterns. This page provides an overview of how the library works and its key components.

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How It Works

1

Feature Extraction

The library extracts features from browser metadata and user behavior (mouse movements, keyboard patterns, scroll behavior, clicks)

2

Data Processing

Extracted features are processed through standardization, normalization, and one-hot encoding where appropriate

3

Model Scoring

A logistic regression model analyzes the processed features to calculate a detection probability

4

Classification

The probability value is compared to a threshold to classify the session as human or AI agent

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Key Components

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Detection Result

When you call AgentDetector.finalizeDetection() or AgentDetector.getCurrentDetectionResult(), you receive a detection result with the following structure:

{
  isAgent: boolean,     // True if the session is classified as an AI agent
  confidence: number,   // Probability prediction between 0 and 1
  features?: any        // Preprocessed features used for scoring (optional)
}

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Next Steps

Detection Model

Learn more about how the detection model works

Feature Extraction

Understand how features are extracted and processed