Emergent Ecological Intelligence: Cross-System Knowledge Discovery from a Network of Environmental AI Agents

We present ENVAI (Environmental AI), a network of nine specialised AI agents, eachrepresenting a distinct European ecosystem—estuarine, riverine, lacustrine, marine, and for-est. Each agent maintains a knowledge graph of sensor data, ecological events, species,environmental drivers, and policy frameworks specific to its ecosystem. By constructing ameta-graph layer (Numina) that maps shared concepts across the nine agent-level graphs, wedemonstrate the emergence of ecological knowledge that exists in no single agent’s dataset.We identify eight shared species appearing across multiple systems, six continental-scale envi-ronmental drivers, five cross-system climate events, four invasion pathways traceable acrossnational borders, and seven recovery timescales spanning four orders of magnitude fromdays to millennia. We argue that a network of domain-specific AI agents, connected througha shared ontological layer, can produce ecological insights that no individual monitoringprogramme, institution, or country can generate alone. We term this capacity emergent eco-logical intelligence and discuss its implications for cross-border environmental governance,early warning systems, and the mapping of continental-scale knowledge gaps.