AI-Agents for Scalable Integration and Decision-Making in Environmental Digital Twins

Environmental digital twins (DTs) model large-scale, complex systems such as cities, ecosystems, or regions, integrating diverse data streams from social, ecological, and infrastructural domains.

The deployment of AI agents within these DTs facilitates the orchestration of intricate data workflows and the contextualisation of multi-source information, supporting long-term planning and policy-making.

AI agents can design and manage scalable data pipelines using open-source frameworks, enabling the enrichment of environmental data with semantic metadata and ontological relationships. This enhances the accessibility and interpretability of data for stakeholders like urban planners, governments, and environmental scientists. Moreover, AI agents can synthesise insights from vast datasets, provide scenario-based recommendations, and issue actionable commands for urban growth, climate adaptation, or resource management.