Learning the Data Environment: Continual Specialization for Data Agents

Data agents often answer a succession of questions about the same data sources. Although they have general analytical capabilities, they often repeat exploratory work across requests, recovering dataset-specific knowledge acquired in earlier analyses. We introduce Continual Data Specialist (CDS), a framework that enables a data agent to acquire reusable, environment-specific state before downstream requests are known. Using multi-table data bundles as a concrete instantiation, CDS generates analysis questions, collects tool-executed trajectories, filters them with lightweight verification, and directs later exploration toward observed failures. The resulting experience is retained through two complementary channels: a bundle-specific LoRA adapter learns patterns of analysis and tool use, while persistent memory statements make dataset-specific findings explicitly available in context. Subsequent requests reuse the same adapter and memory bank. For each request, a hypothesis module draws on both channels to propose analysis directions, which are checked against the original data through tool execution. Experiments on three data-agent benchmarks show that CDS improves insight quality over Vanilla Agent and either memory channel alone. CDS offers a route from general-purpose analysis to environment-specific expertise, turning autonomous exploration into knowledge that can support repeated use of the same data.