Build a 4-agent AI system to fight insurance discrimination, with full observability and bias tracking powered by W&B Weave.

Screen Recording 2025-07-13 at 7.18.25 AM.mov

Introduction

This project showcases the development of a multi-agent AI system designed to detect and combat insurance discrimination. Using W&B Weave, we will build a production-grade application with detailed tracing, bias monitoring, and real-time performance metrics.

Key W&B Weave Features Demonstrated:

• 🔍 Agent Tracing: Monitor the execution flow and performance of all four agents.
• ⚖️ Bias Monitoring: Track and identify patterns of bias across different demographics.
• 📊 Performance Tracking: Observe real-time latency and accuracy metrics.
• 🎯 A/B Testing: Compare different algorithms to improve fairness and accuracy.
• 📝 Audit Trails: Generate legal-grade logs for compliance and transparency.

The Challenge

Mission: Build an AI system that uncovers and prevents insurance discrimination against wildfire victims in Altadena, California.

The Problem: Altadena's ~43,000 residents face systematic insurance bias. Data shows claim denial rates of 18.5% for Altadena compared to just 3.2% in more affluent areas. Claim processing times are also significantly longer, averaging 72 days versus 31 days elsewhere.

Your Solution: A 4-agent command-line system that provides real-time bias detection, objective risk assessment, and legal-grade evidence to ensure fair treatment for all claimants.

System Architecture

The system is composed of four distinct agents that work in sequence. W&B Weave monitors the entire workflow for performance, tracing, and bias detection.

Agent Workflow:

🔥 Agent 1   ▶️   ⚖️ Agent 2   ▶️   📚 Agent 3   ▶️   🎯 Agent 4
Fire Risk        Bias Detection     Claims Analysis    Action Coordinator
(500ms)          (800ms)            (1200ms)           (400ms)

Monitoring Layer:

    │                │                  │                  │
    └─────── W&B Weave Observability & Tracing ───────┘