[2603.24856] SentinelAI: A Multi-Agent Framework for Structuring and Linking NG9-1-1 Emergency Incident Data

Computer Science > Artificial Intelligence arXiv:2603.24856 (cs) [Submitted on 25 Mar 2026] Title:SentinelAI: A Multi-Agent Framework for Structuring and Linking NG9-1-1 Emergency Incident Data Authors:Kliment Ho, Ilya Zaslavsky View a PDF of the paper titled SentinelAI: A Multi-Agent Framework for Structuring and Linking NG9-1-1 Emergency Incident Data, by Kliment Ho and Ilya Zaslavsky View PDF HTML (experimental) Abstract: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. Comments: Subjects: Artificial Intelligence (cs.AI); Computers and Society (cs.CY); Emerging Technologies (cs.ET); Multiagent Systems (cs.MA) Cite as: arXiv:2603.24856 [cs.AI]   (or arXiv:2603.24856v1 [cs.A...