SentinelAI: A Multi-Agent Framework for Structuring and Linking NG9-1-1 Emergency Incident Data
Fuente:
arXiv
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866914422729998336 |
|---|---|
| author | Ho, Kliment Zaslavsky, Ilya |
| author_facet | Ho, Kliment Zaslavsky, Ilya |
| contents | Emergency response systems generate data from many agencies and systems. In practice, correlating and updating this information across sources in a way that aligns with Next Generation 9-1-1 data standards remains challenging. Ideally, this data should be treated as a continuous stream of operational updates, where new facts are integrated immediately to provide a timely and unified view of an evolving incident. This paper presents SentinelAI, a data integration and standardization framework for transforming emergency communications into standardized, machine-readable datasets that support integration, composite incident construction, and cross-source reasoning. SentinelAI implements a scalable processing pipeline composed of specialized agents. The EIDO Agent ingests raw communications and produces NENA-compliant Emergency Incident Data Object JSON. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_24856 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | SentinelAI: A Multi-Agent Framework for Structuring and Linking NG9-1-1 Emergency Incident Data Ho, Kliment Zaslavsky, Ilya Artificial Intelligence Computers and Society Emerging Technologies Multiagent Systems Emergency response systems generate data from many agencies and systems. In practice, correlating and updating this information across sources in a way that aligns with Next Generation 9-1-1 data standards remains challenging. Ideally, this data should be treated as a continuous stream of operational updates, where new facts are integrated immediately to provide a timely and unified view of an evolving incident. This paper presents SentinelAI, a data integration and standardization framework for transforming emergency communications into standardized, machine-readable datasets that support integration, composite incident construction, and cross-source reasoning. SentinelAI implements a scalable processing pipeline composed of specialized agents. The EIDO Agent ingests raw communications and produces NENA-compliant Emergency Incident Data Object JSON. |
| title | SentinelAI: A Multi-Agent Framework for Structuring and Linking NG9-1-1 Emergency Incident Data |
| topic | Artificial Intelligence Computers and Society Emerging Technologies Multiagent Systems |
| url | https://arxiv.org/abs/2603.24856 |