Rapid identification of hazardous events is essential for next-generation Earth Observation (EO) missions supporting disaster response. However, current monitoring pipelines remain largely ground-centric, introducing latency due to downlink limitations, multi-source data fusion constraints, and the computational cost of exhaustive scene analysis. This work proposes a hierarchical multi-agent architecture for onboard EO processing under strict resource and bandwidth constraints. The system enables the exploitation of complementary multimodal observations by coordinating specialized AI agents within an event-driven decision pipeline. AI agents can be deployed across multiple nodes in a distributed setting, such as satellite platforms. An Early Warning agent generates fast hypotheses from onboard observations and selectively activates domain-specific analysis agents, while a Decision agent consolidates the evidence to issue a final alert. The architecture combines vision-language models, traditional remote sensing analysis tools, and role-specialized agents to enable structured reasoning over multimodal observations while minimizing unnecessary computation. A proof-of-concept implementation was executed on the engineering model of an edge-computing platform currently deployed in orbit, using representative satellite data. Experiments on wildfire and flood monitoring scenarios show that the proposed routing-based pipeline significantly reduces computational overhead while maintaining coherent decision outputs, demonstrating the feasibility of distributed agent-based reasoning for future autonomous EO constellations.

翻译：快速识别危险事件对于支持灾害响应的下一代地球观测（EO）任务至关重要。然而，当前的监测流程仍以地面为中心，因下行链路限制、多源数据融合约束及全面场景分析的巨大计算开销而导致延迟。本文提出一种在严格资源与带宽约束下，面向在轨EO处理的分层多智能体架构。该系统通过协调事件驱动决策流程中的专业化AI智能体，实现对互补多模态观测数据的有效利用。AI智能体可分布式部署于多个节点（如卫星平台）。预警智能体基于在轨观测快速生成假设，并选择性激活领域分析智能体，而决策智能体则综合证据以发出最终警报。该架构融合视觉-语言模型、传统遥感分析工具及角色专业化智能体，在最大限度减少非必要计算的同时，实现对多模态观测数据的结构化推理。基于当前在轨部署的边缘计算平台工程模型，利用代表性卫星数据进行了概念验证实现。针对野火与洪涝监测场景的实验表明，所提出的路由型流程在保持决策输出连贯性的同时显著降低了计算开销，验证了分布式智能体推理用于未来自主EO星座的可行性。