Data Agents: Agentic Data Systems

arXiv:2609.24137 · cs.DB, cs.AI, cs.CL, cs.LG · Submitted 2026-09-21 · Read on arXiv

cs.DB, cs.AI, cs.CL, cs.LG

Submitted: 2026-09-21

Updated: 2026-09-21

Comments: Accepted by TKDE

DOI: 10.1109/TKDE.2026.3736532

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Memory Management — 22× in this paper
• Adaptation — 17× in this paper · explained in 1 episode
• Benchmark — 12× in this paper · explained in 2 episodes
• Accuracy — 11× in this paper · explained in 1 episode
• Consistency — 10× in this paper · explained in 3 episodes
• Embeddings — 8× in this paper · explained in 2 episodes
• RAG — 6× in this paper · explained in 3 episodes
• Ablation — 5× in this paper · explained in 1 episode
• Text-to-SQL — 5× in this paper · explained in 3 episodes
• Ablation study — 4× in this paper · explained in 1 episode
• Aggregation — 4× in this paper · explained in 1 episode
• Core — 4× in this paper · explained in 2 episodes
• Grounding — 4× in this paper · explained in 3 episodes
• Robustness — 4× in this paper · explained in 5 episodes
• Semantic Similarity — 4× in this paper · explained in 2 episodes
• Semantic Understanding — 4× in this paper · explained in 2 episodes
• Verification — 4× in this paper · explained in 2 episodes
• Agent Memory — 3× in this paper · explained in 1 episode
• Alignment — 3× in this paper · explained in 1 episode
• Cosine Similarity — 2× in this paper · explained in 3 episodes
• Distribution Shift — 2× in this paper · explained in 5 episodes
• Interpretability — 2× in this paper · explained in 15 episodes
• Knowledge Graph — 2× in this paper · explained in 7 episodes
• Knowledge graphs — 2× in this paper · explained in 2 episodes
• Multi-Agent Framework — 2× in this paper · explained in 3 episodes
• Personalization — 2× in this paper · explained in 2 episodes
• Pruning — 2× in this paper · explained in 4 episodes
• Query Complexity — 2× in this paper · explained in 2 episodes
• Redundancy — 2× in this paper · explained in 2 episodes
• Task Decomposition — 2× in this paper · explained in 2 episodes
• AI Agents — 1× in this paper · explained in 2 episodes
• Abundance — 1× in this paper · explained in 1 episode
• Adaptability — 1× in this paper · explained in 1 episode
• Agentic Memory System — 1× in this paper · explained in 1 episode
• Closed-Loop System — 1× in this paper · explained in 4 episodes
• Credit assignment — 1× in this paper · explained in 3 episodes
• Feature Engineering — 1× in this paper · explained in 3 episodes
• Fidelity — 1× in this paper · explained in 2 episodes
• Fine-Tuning — 1× in this paper · explained in 5 episodes
• Hallucination — 1× in this paper · explained in 3 episodes
• Human-in-the-Loop — 1× in this paper · explained in 3 episodes
• In-Context Learning — 1× in this paper · explained in 4 episodes
• KV cache — 1× in this paper · explained in 3 episodes
• Model Context Protocol — 1× in this paper
• Model Context Protocol (MCP) — 1× in this paper · explained in 8 episodes
• Named Entity Recognition (NER) — 1× in this paper · explained in 2 episodes
• Provenance — 1× in this paper · explained in 3 episodes
• Resource Constraints — 1× in this paper · explained in 3 episodes
• Semantic Alignment — 1× in this paper · explained in 3 episodes
• Topological Order — 1× in this paper · explained in 2 episodes
• Trace — 1× in this paper · explained in 2 episodes
• Transferability — 1× in this paper · explained in 4 episodes
• Unified Framework — 1× in this paper · explained in 5 episodes
• Vector Representations — 1× in this paper · explained in 2 episodes

Sources

• TableMaster: A Recipe to Advance Table Understanding with Language Models
• Query Rewriting via LLMs
• Text-to-Pipeline: Bridging Natural Language and Data Preparation Pipelines
• DS-Agent: Automated Data Science by Empowering Large Language Models with Case-Based Reasoning
• ChatGPT as your Personal Data Scientist
• Leveraging Large Language Models for Entity Matching
• Alpha-SQL: Zero-Shot Text-to-SQL using Monte Carlo Tree Search
• AutoKaggle: A Multi-Agent Framework for Autonomous Data Science Competitions
• Large Language Model Agent: A Survey on Methodology, Applications and Challenges
• The RefinedWeb Dataset for Falcon LLM: Outperforming Curated Corpora with Web Data, and Web Data Only
• AgenticData: An Agentic Data Analytics System for Heterogeneous Data
• LAMBDA: A Large Model Based Data Agent
• VisJudge-Bench: Aesthetics and Quality Assessment of Visualizations
• Query Performance Explanation through Large Language Model for HTAP Systems
• DataPuzzle: Breaking Free from the Hallucinated Promise of LLMs in Data Analysis
• GaussMaster: An LLM-based Database Copilot System
• A Survey of LLM $\times$ DATA
• A Survey of Data Agents: Emerging Paradigm or Overstated Hype?
• A Survey of Self-Evolving Agents: What, When, How, and Where to Evolve on the Path to Artificial Super Intelligence
• DeepEye-SQL: A Software-Engineering-Inspired Text-to-SQL Framework

Related papers

• Vibe Coding on Trial: Operating Characteristics of Unanimous LLM Juries
• Human-Level Text-to-SQL via Reinforcement Learning on Verified Data, Without Pipeline Engineering
• Bridging Business Intent and Data: A Benchmark for Automatic Relational Data Product Generation
• DexterSQL: Deep Schema Exploration and Rule-based Correction for Text-to-SQL Generation
• MaDI-Bench: An End-to-End Data Integration Benchmark
• Eigenius: A Typed Knowledge-Graph DBMS with Epistemic Stratification and Institution-Mediated Reasoning