HARNESS: Human-Agent Risk Navigation and Event Safety System for Proactive Hazard Forecasting in High-Risk DOE Environments

TL;DR

HARNESS is an AI framework that combines large language models, structured data, and expert input to proactively forecast hazards and improve safety in high-risk DOE environments.

Contribution

The paper introduces HARNESS, a modular AI system integrating LLMs and human-in-the-loop mechanisms for proactive hazard prediction in complex operational settings.

Findings

Preliminary deployment shows promising hazard forecasting capabilities.

The system enhances safety decision-making through iterative learning.

Future work aims to quantitatively evaluate accuracy and efficiency.

Abstract

Operational safety at mission-critical work sites is a top priority given the complex and hazardous nature of daily tasks. This paper presents the Human-Agent Risk Navigation and Event Safety System (HARNESS), a modular AI framework designed to forecast hazardous events and analyze operational risks in U.S. Department of Energy (DOE) environments. HARNESS integrates Large Language Models (LLMs) with structured work data, historical event retrieval, and risk analysis to proactively identify potential hazards. A human-in-the-loop mechanism allows subject matter experts (SMEs) to refine predictions, creating an adaptive learning loop that enhances performance over time. By combining SME collaboration with iterative agentic reasoning, HARNESS improves the reliability and efficiency of predictive safety systems. Preliminary deployment shows promising results, with future work focusing on quantitative evaluation of accuracy, SME agreement, and decision latency reduction.