International Journal of Scientific Research and Engineering Development

Environmental monitoring increasingly depends on heterogeneous, high-volume data streams — satellite imagery, ground sensor networks, weather telemetry, and unstructured text — that must be fused and interpreted in near real time to detect hazardous anomalies such as flash floods, wildfires, air-quality excursions, and seismic precursors. Single-model and single-agent pipelines struggle to scale across this heterogeneity: they conflate perception, reasoning, and domain expertise into one monolithic process, which limits interpretability, fault isolation, and extensibility. This paper presents the Planetary Intelligence System (PIS), a reproducible multi-agent architecture in which a supervisor agent orchestrates a pool of specialist agents — perception, geo-spatial, temporal-pattern, and domain agents for hydrology, wildfire, air quality, and seismic activity — coordinated through a typed message bus, a tiered short-term/long-term/vector memory system, and a retrieval-augmented knowledge layer. We detail the orchestration algorithms (task decomposition, capability-based routing, weighted-vote consensus, and conflict resolution), a reference implementation built on containerized microservices, and an evaluation across four simulated and two real-world-derived environmental datasets. Compared with a single-agent large language model baseline and two existing multi-agent frameworks, PIS improves anomaly-detection F1 by 9.4 to 16.7 percentage points, reduces end-to-end latency by 31%, and lowers false-positive rate by more than half, while an ablation study shows that removing the orchestrator's consensus mechanism or the shared vector memory degrades accuracy the most among all tested components. We discuss failure modes, security and privacy considerations, and directions for self-learning and dynamically created agents.

Jahnavi Somaraju, V. Rohini, "Toward Planetary-Scale Intelligence: A Multi-Agent Framework for Cross-Domain Environmental Anomaly Detection and Reasoning" International Journal of Scientific Research and Engineering Development, V9(4): Page(319-327) May-June 2026. ISSN: 2581-7175. www.ijsred.com. Published by Scientific and Academic Research Publishing.