mlsanityData to 14 Oct 2026

Can AI autonomously build, operate, and use the entire data stack?

arXiv 2512.07926 ↗First posted 8 Dec 2025cs.AI cs.DB cs.LGcited by 0

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Abstract (arXiv)

Enterprise data management is a monumental task. It spans data architecture and systems, integration, quality, governance, and continuous improvement. While AI assistants can help specific persona, such as data engineers and stewards, to navigate and configure the data stack, they fall far short of full automation. However, as AI becomes increasingly capable of tackling tasks that have previously resisted automation due to inherent complexities, we believe there is an imminent opportunity to target fully autonomous data estates. Currently, AI is used in different parts of the data stack, but in this paper, we argue for a paradigm shift from the use of AI in independent data component operations towards a more holistic and autonomous handling of the entire data lifecycle. Towards that end, we explore how each stage of the modern data stack can be autonomously managed by intelligent agents to build self-sufficient systems that can be used not only by human end-users, but also by AI itself. We begin by describing the mounting forces and opportunities that demand this paradigm shift, examine how agents can streamline the data lifecycle, and highlight open questions and areas where additional research is needed. We hope this work will inspire lively debate, stimulate further research, motivate collaborative approaches, and facilitate a more autonomous future for data systems.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 17 of its 38 references that are in the map. First posted on arXiv 8 Dec 2025.

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17 of 38 references.

References · 17

  1. 2023AgentBench: Evaluating LLMs as Agentscited by 1,267
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  4. 2024Spider 2.0: Evaluating Language Models on Real-World Enterprise Text-to-SQL Workflowscited by 232
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  6. 2024DocETL: Agentic Query Rewriting and Evaluation for Complex Document Processingcited by 52
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  9. 2024λ-Tune: Harnessing Large Language Models for Automated Database System Tuningcited by 19
  10. 2025BIRD-INTERACT: Re-imagining Text-to-SQL Evaluation for Large Language Models via Lens of Dynamic Interactionscited by 19
  11. 2025ELT-Bench: An End-to-End Benchmark for Evaluating AI Agents on ELT Pipelinescited by 16
  12. 2025SWE-SQL: Illuminating LLM Pathways to Solve User SQL Issues in Real-World Applicationscited by 16
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  1. 2022LlamaTune: Sample-Efficient DBMS Configuration Tuningcited by 14
  2. 2024DePLOI: Applying NL2SQL to Synthesize and Audit Database Access Controlcited by 7
  3. 2024NeurDB: On the Design and Implementation of an AI-powered Autonomous Databasecited by 5
  4. 2025LEDD: Large Language Model-Empowered Data Discovery in Data Lakescited by 4
  5. 2025Text2Schema: Filling the Gap in Designing Database Table Structures based on Natural Languagecited by 2

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