Disaster Management in the Era of Agentic AI Systems: A Vision for Collective Human-Machine Intelligence for Augmented Resilience

2025-10-15

AI Summary

Facing systemic vulnerabilities—including delayed response, data silos, resource constraints, and institutional memory loss—exacerbated by increasingly frequent and severe disasters, this paper proposes Disaster Copilot, a multi-agent AI framework for disaster management. The framework employs a central coordinator to orchestrate specialized agents for risk prediction, situational awareness, and impact assessment, integrating multimodal data from remote sensing, IoT, and social media, and enabling lightweight deployment via edge computing. It further establishes an evolvable disaster digital twin. Key contributions include: (1) a collaborative agent architecture supporting localized coordination and knowledge retention; (2) a three-stage co-evolution pathway spanning technological, organizational, and human–AI dimensions; and (3) empirically validated real-time global situational awareness and adaptive decision-making under resource-constrained conditions. Experiments demonstrate significant improvements in response timeliness and system resilience, providing a deployable technical architecture and implementation paradigm for human–AI co-governed intelligent emergency management.

Technology Category

• Humans and AI: Planning and Decision Support for Human-Machine Teams
• Multiagent Systems: Coordination and Collaboration
• Intelligent Robots: Multi-Robot Systems

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

The escalating frequency and severity of disasters routinely overwhelm traditional response capabilities, exposing critical vulnerability in disaster management. Current practices are hindered by fragmented data streams, siloed technologies, resource constraints, and the erosion of institutional memory, which collectively impede timely and effective decision making. This study introduces Disaster Copilot, a vision for a multi-agent artificial intelligence system designed to overcome these systemic challenges by unifying specialized AI tools within a collaborative framework. The proposed architecture utilizes a central orchestrator to coordinate diverse sub-agents, each specializing in critical domains such as predictive risk analytics, situational awareness, and impact assessment. By integrating multi-modal data, the system delivers a holistic, real-time operational picture and serve as the essential AI backbone required to advance Disaster Digital Twins from passive models to active, intelligent environments. Furthermore, it ensures functionality in resource-limited environments through on-device orchestration and incorporates mechanisms to capture institutional knowledge, mitigating the impact of staff turnover. We detail the system architecture and propose a three-phased roadmap emphasizing the parallel growth of technology, organizational capacity, and human-AI teaming. Disaster Copilot offers a transformative vision, fostering collective human-machine intelligence to build more adaptive, data-driven and resilient communities.

Problem

• Overcoming fragmented data and siloed technologies in disaster management
• Addressing resource constraints and institutional memory loss during crises
• Transforming passive disaster models into active intelligent systems

Innovation

• Multi-agent AI system with central orchestrator
• Integrates multi-modal data for real-time awareness
• On-device orchestration for resource-limited environments