Data World Model: Enhancing Data Agents through Workspace Modeling

LLM-based data agents must complete tasks within workspaces containing heterogeneous data assets. A workspace is initially opaque to the agent and is far too large to fit within a context window, posing significant challenges for data agents. Existing approaches either explore the workspace through costly trial-and-error interactions or construct a fixed semantic layer prior to execution, and both are limited to representing only the information of a workspace. We advance data agents from workspace representation to workspace modeling by introducing the Data World Model (DWM), an agent-first model for data workspaces. Following the three constituents of a classical data model, DWM integrates a measured state that captures workspace assets and their relationships, an operation model that predicts how an intended action transforms the current state, and a constraint model that determines whether a generated artifact meets the deliverable conditions the task states. DWM wraps the agent's function calling loop, monitors and processes the interactions between the agent and the workspace, and continuously updates itself based on the interaction content. Experiments across seven benchmarks demonstrate that DWM substantially improves data agents, with the largest gains observed in complex workspaces where direct observation is infeasible.