agentic-ai-device-analysis

Agentic AI Device Attributes Analysis Demo

A comprehensive demonstration of an Agentic AI system for device behavior analysis and fraud prevention. This package provides an interactive web-based demo that showcases how AI agents can analyze device attributes to detect suspicious behavior and potential fraud.

Features

• Interactive Web Interface: Clean, modern UI for device analysis demonstration
• Real-time Analysis: Simulated AI-powered device attribute analysis
• Multiple Risk Levels: Supports normal, suspicious, and fraudulent device scenarios
• Agentic AI Simulation: Demonstrates multi-agent decision-making processes
• Zero Dependencies: Uses only Python standard library for maximum compatibility
• Easy Deployment: Simple installation and deployment options

Installation

From PyPI (when published)

pip install agentic-ai-device-analysis

From Source

git clone https://github.com/deviceanalysis/agentic-ai-device-analysis.git
cd agentic-ai-device-analysis
pip install -e .

Development Installation

git clone https://github.com/deviceanalysis/agentic-ai-device-analysis.git
cd agentic-ai-device-analysis
pip install -e ".[dev]"

Quick Start

Command Line

After installation, you can start the demo server using:

# Using the main command
agentic-demo

# Alternative command
device-analysis-demo

# With custom host and port
agentic-demo --host 0.0.0.0 --port 8080

Python Module

# Run as module
python -m agentic_ai_demo

# With arguments
python -m agentic_ai_demo --host localhost --port 8000

Direct Script Execution

python agentic_ai_demo/server.py

Usage

1. Start the server using any of the methods above
2. Open your web browser and navigate to http://localhost:8000
3. Use the interactive interface to analyze different device scenarios:
   • Normal Device: Simulates a legitimate user device
   • Suspicious Device: Shows elevated risk indicators
   • Fraudulent Device: Demonstrates high-risk fraud patterns

API Endpoints

• GET / - Main demo interface
• GET /api/analyze?type={normal|suspicious|fraud} - Device analysis API
• GET /health - Health check endpoint

Demo Features

Device Analysis Types

1. Normal Device Analysis

   • Low risk score (0.1-0.3)
   • Standard device fingerprinting
   • Typical user behavior patterns

2. Suspicious Device Analysis

   • Medium risk score (0.4-0.7)
   • Anomalous behavior detection
   • Enhanced monitoring recommendations

3. Fraudulent Device Analysis

   • High risk score (0.8-0.95)
   • Multiple fraud indicators
   • Immediate action recommendations

AI Agent Simulation

The demo simulates various AI agents working together:

• Device Fingerprint Agent: Analyzes hardware and software characteristics
• Behavior Analysis Agent: Monitors user interaction patterns
• Risk Assessment Agent: Calculates overall fraud probability
• Decision Engine: Coordinates agent findings and recommendations

Architecture