Demonstration of Pneuma-Seeker: Agentic System for Reifying and Fulfilling Information Needs on Tabular Data
Muhammad Imam Luthfi Balaka (The University of Chicago), Raul Castro Fernandez (The University of Chicago)
Architectural Patterns & Composition
Summary
An agentic system that reifies vague user information needs as inspectable relational specifications for iterative tabular data discovery and provenance-aware execution.
Description
Data analysts working with relational data often start with vague or underspecified questions and refine them iteratively as they explore the data. To support this iterative process, we demonstrate Pneuma-Seeker, a system that reifies a user's information need as explicit, inspectable relational specifications, enabling iterative refinement of the information need, targeted data discovery, and provenance-aware execution. Through two real-world procurement use cases, we show how Pneuma-Seeker leverages LLMs as transparent, interactive analytical collaborators rather than opaque answer engines.
