Building Connected Earth Observation Ecosystems with Agentic AI using EVE

This study explores the integration of EVE (Earth Virtual Expert), a Large Language Model specialized in Earth Observation (EO) and Earth Sciences, developed under ESA’s Φ-lab #AI4EO initiative in collaboration with Pi School. The primary objective is to enable EVE to connect ESA’s EO platforms and data clusters, creating an integrated ecosystem for the community. This approach leverages agentic capabilities, allowing EVE to dynamically interact with EO tools, databases, and APIs to reason and act autonomously.
To demonstrate this concept, we present a use case where EVE operates within an agentic framework to interact with the EO Dashboard, a joint initiative by ESA, NASA, and JAXA that provides global indicators and narratives derived from multi-mission EO data. Using the MCP protocol, this work enables dynamic connectivity between EVE and the Dashboard, allowing the model to interpret and summarize narratives, extend insights with additional context, and facilitate advanced information retrieval across datasets and stories. In addition, the study considers potential directions for agentic behaviors, assessing early-stage possibilities and limitations for features such as autonomous task chaining. These capabilities enable EVE to perform multi-step reasoning, for example, by interpreting quantitative trends in dashboard indicators such as air quality changes, greenhouse gas concentrations, or land cover dynamics. This links EVE to underlying datasets and enables the generation of scientifically grounded responses. This proof-of-concept demonstrates EVE’s potential to foster interoperability and accelerate Earth system science by improving knowledge accessibility and enabling more effective use of EO data resources.