Building a 13-Agent AI System for Real-Time Road Safety Monitoring

Kerala, India has one of the highest road accident rates in the country — over 40,000 accidents annually across its narrow, winding highways. I built SurakshaNet , a multi-agent intelligence platform that monitors 6 high-risk road segments in real time using 13 AI agents, Byzantine fault-tolerant voting, and Bayesian belief fusion. This post covers the architecture, the problems I solved, and what I learned. The Problem Traditional road safety systems rely on single data sources — a camera feed or a weather alert. But accidents are multi-causal. A wet road alone is not dangerous. A wet road at night, near a school zone, with heavy traffic and poor visibility — that is dangerous. I needed a system that could fuse multiple independent signals into a single calibrated risk score, attribute causality, and trigger the right response automatically. Architecture Overview The system runs 3 agent clusters in parallel per road segment, every 5 minutes: SWARM-GUARD (Road Safety): Weather friction