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

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.

Figures (5)

• Figure 1: SentinelAI reference architecture showing the three-agent workflow. The EIDO Agent structures incoming reports into EIDO-JSON, the IDX Agent links related EIDOs into composite incidents, and the Geocoding Agent enriches location information. Agents interact through well-defined interfaces and support analytical and integration services.
• Figure 2: Decision logic employed by the IDX Agent to determine if an incoming report represents a new incident or an update to an existing one.
• Figure 3: Integration with FME showing EIDOReader and EIDOWriter components facilitating bidirectional data exchange.
• Figure 4: Visual transformation of the NWS Flood Warning text (left) into a structured EIDO-JSON document (right) by the EIDO Agent, highlighting the extracted event type and polygon location data.
• Figure 5: The SentinelAI Dashboard visualization showing the IDX Agent's linking of the NWS Warning and the News Report into a single composite incident timeline.