Enhancing Operational Safety via Agentic Dialogue Hazard Identification Analysis

2026-06-02

AI Summary

This study addresses the insufficient reliability of hazard identification in high-stakes domains—such as industrial control and autonomous driving—where existing single-pass large model reasoning lacks the iterative self-correction and contextual refinement characteristic of human engineers. To overcome this limitation, the authors propose HAZDIAL, a novel framework that introduces, for the first time, a structured multi-agent, multi-turn dialogue system for hazard identification. HAZDIAL replaces one-shot inference with adversarial debate and constructive discussion mechanisms, complemented by an agent interaction optimization algorithm and new dialogue evaluation metrics. Experimental results demonstrate that the approach significantly outperforms single-round baselines across standard classification metrics (accuracy, precision, recall, F1) as well as dialogue quality measures, establishing a new paradigm of dialogue-driven safety analysis and providing empirical support for integrating AI safety with multi-agent reasoning.

Abstract

Operational safety in high-stakes domains such as industrial process control, autonomous, and safety-critical systems, demand reliable hazard identification. While large language models (LLMs) have shown promise in automating safety analysis tasks, single-turn, monolithic inference is brittle: it lacks the self-correction, deliberation, and contextual refinement that safety engineers apply iteratively. In this paper, we introduce HAZDIAL, a framework that investigates whether structured agentic dialogue-multi-agent, multi-turn interactions improves the quality of NLP- based hazard identification over single-pass baselines. We systematically compare two dialogue modalities: adversarial debate and constructive discussion, and propose an algorithm-based agentic interaction optimization. We evaluate all configurations against a curated golden dataset using standard classification metrics (accuracy, precision, recall, F1) and novel dialogue metrics. This work advances the intersection of dialogue systems, multi-agent reasoning, and AI safety, providing an empirical evidence for dialogue-driven hazard analysis.

Problem

• hazard identification
• operational safety
• large language models
• multi-agent dialogue
• AI safety

Innovation

• agentic dialogue
• hazard identification
• multi-agent reasoning
• AI safety
• structured debate