Agentic analytics is turning the lakehouse into a multi-version system: swarms of (human or AI) producers materialize competing pipelines in data branches, while (human or AI) consumers need answers without knowing the underlying data life-cycle. We demonstrate a new system that answers questions across branches rather than at a single snapshot. Our prototype focuses on a novel query path that evaluates queries under supervaluationary semantics. In the absence of comparable multi-branch querying capabilities in mainstream OLAP systems, we open source the demo code as a concrete baseline for the OLAP community.
@article{arxiv.2603.13380,
title = {Querying Everything Everywhere All at Once: Supervaluationism for the Agentic Lakehouse},
author = {Jacopo Tagliabue},
journal= {arXiv preprint arXiv:2603.13380},
year = {2026}
}