paper-with-me

Papers

GitLake: Git-for-data for the agentic lakehouse

2026-07-09 · Weiming Sheng, Jinlang Wang, Manuel Barros, Aldrin Montana, Jacopo Tagliabue, Luca Bigon arxiv

We present GitLake, a Git-for-data design for an agent-first lakehouse. The system lifts single-table Iceberg snapshots into lakehouse-wide commits, branches, and merges, letting agents work on isolated branches while humans review and publish changes. Pipelines run on temporary branches and publish through a final merge, so all outputs become visible atomically or none do. Finally, we report production lessons as well as correctness insights from a preliminary Alloy model of our core abstractions.

📄 PDF Abstract BibTeX arXiv:2607.08319

Code (0)

등록된 구현이 없습니다.

Similar Papers 제목 키워드 기반

Safe, Untrusted, "Proof-Carrying" AI Agents: toward the agentic lakehouse

2025-10-10 · Jacopo Tagliabue, Ciro Greco arxiv

Data lakehouses run sensitive workloads, where AI-driven automation raises concerns about trust, correctness, and governance. We argue that API-first, programmable lakehouses provide the right abstractions for safe-by-de…

Trustworthy AI in the Agentic Lakehouse: from Concurrency to Governance

2025-11-20 · Jacopo Tagliabue, Federico Bianchi, Ciro Greco arxiv

Even as AI capabilities improve, most enterprises do not consider agents trustworthy enough to work on production data. In this paper, we argue that the path to trustworthy agentic workflows begins with solving the infra…

Querying Everything Everywhere All at Once: Supervaluationism for the Agentic Lakehouse

2026-03-11 · Jacopo Tagliabue arxiv

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 u…

"Skill Issues'': Data-Centric Optimization of Lakehouse Agents

2026-05-31 · Nicole Rose Schneider, Davide Ghilardi, Giacomo Piccinini, Jacopo Tagliabue arxiv

Coding agents are becoming users of data infrastructure, but their success depends not only on model quality: it also depends on the skills and environment files that teach agents how to use a system. We study how to opt…

Building a Correct-by-Design Lakehouse. Data Contracts, Versioning, and Transactional Pipelines for Humans and Agents

2026-02-02 · Weiming Sheng, Jinlang Wang, Manuel Barros, Aldrin Montana 외 arxiv

Lakehouses are now the default substrate for analytics and AI, but they remain fragile under concurrent, untrusted change: schema mismatches often surface only at runtime, development and production easily diverge, and m…