# AI Agents in Emergency Response Applications

arXiv: https://arxiv.org/abs/2109.04646 · HTML: https://mlsanity.com/paper/2109.04646/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 20 references that are in the map.
- First posted on arXiv 10 Sep 2021.

## Built on by (most cited first, 1 of 1)

- [ChEmREF: Evaluating Language Model Readiness for Chemical Emergency Response](https://mlsanity.com/paper/2511.10027/) (2025)

## Abstract (arXiv)

Emergency personnel respond to various situations ranging from fire, medical, hazardous materials, industrial accidents, to natural disasters. Situations such as natural disasters or terrorist acts require a multifaceted response of firefighters, paramedics, hazmat teams, and other agencies. Engineering AI systems that aid emergency personnel proves to be a difficult system engineering problem. Mission-critical "edge AI" situations require low-latency, reliable analytics. To further add complexity, a high degree of model accuracy is required when lives are at stake, creating a need for the deployment of highly accurate, however computationally intensive models to resource-constrained devices. To address all these issues, we propose an agent-based architecture for deployment of AI agents via 5G service-based architecture.