SENTINELAI: A MULTI-AGENT FRAMEWORK FOR STRUCTURING AND LINKING NG9-1-1 EMERGENCY INCIDENT DATA

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. 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. The Incident Data Exchange Agent performs incident correlation across EIDOs, and the Geocoding Agent provides spatial enrichment. The contributions of this work are twofold: first, the operational treatment of EIDO as an evolving incident representation that is updated incrementally as new information arrives, rather than as a single static exchange object; and second, the implementation of an agent-based reference architecture aligned with the recently adopted NENA EIDO standard. This work focuses on data structuring and interoperability, rather than operational decision-making or predictive performance. We demonstrate a reference implementation of the three agents and an integration with FME, enabling interoperability with existing geospatial and emergency management systems.