# Data Intelligence Agents: Interpreting, Modeling, and Querying Enterprise Data via Autonomous Coding Agents

arXiv: https://arxiv.org/abs/2606.19319 · HTML: https://mlsanity.com/paper/2606.19319/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Web, search, and research agents, one of 16 directions in LLM agents and tool use.
- Builds on 34 of its 41 references that are in the map.
- First posted on arXiv 17 Jun 2026.

## Placed in

- LLM agents and tool use / [Web, search, and research agents](https://mlsanity.com/agents/web-search-and-research-agents/)

## Builds on (most cited first, 30 of 34)

- [Can LLM Already Serve as A Database Interface? A BIg Bench for Large-Scale Database Grounded Text-to-SQLs](https://mlsanity.com/paper/2305.03111/) (2023)
- [Executable Code Actions Elicit Better LLM Agents](https://mlsanity.com/paper/2402.01030/) (2024)
- [DIN-SQL: Decomposed In-Context Learning of Text-to-SQL with Self-Correction](https://mlsanity.com/paper/2304.11015/) (2023)
- [Spider 2.0: Evaluating Language Models on Real-World Enterprise Text-to-SQL Workflows](https://mlsanity.com/paper/2411.07763/) (2024)
- [CoSQL: A Conversational Text-to-SQL Challenge Towards Cross-Domain Natural Language Interfaces to Databases](https://mlsanity.com/paper/1909.05378/) (2019)
- [CHESS: Contextual Harnessing for Efficient SQL Synthesis](https://mlsanity.com/paper/2405.16755/) (2024)
- [MAC-SQL: A Multi-Agent Collaborative Framework for Text-to-SQL](https://mlsanity.com/paper/2312.11242/) (2023)
- [SParC: Cross-Domain Semantic Parsing in Context](https://mlsanity.com/paper/1906.02285/) (2019)
- [CHASE-SQL: Multi-Path Reasoning and Preference Optimized Candidate Selection in Text-to-SQL](https://mlsanity.com/paper/2410.01943/) (2024)
- [Next-Generation Database Interfaces: A Survey of LLM-based Text-to-SQL](https://mlsanity.com/paper/2406.08426/) (2024)
- [OmniSQL: Synthesizing High-quality Text-to-SQL Data at Scale](https://mlsanity.com/paper/2503.02240/) (2025)
- [OpenSearch-SQL: Enhancing Text-to-SQL with Dynamic Few-shot and Consistency Alignment](https://mlsanity.com/paper/2502.14913/) (2025)
- [A Preview of XiYan-SQL: A Multi-Generator Ensemble Framework for Text-to-SQL](https://mlsanity.com/paper/2411.08599/) (2024)
- [ReFoRCE: A Text-to-SQL Agent with Self-Refinement, Consensus Enforcement, and Column Exploration](https://mlsanity.com/paper/2502.00675/) (2025)
- [Understanding the Effects of Noise in Text-to-SQL: An Examination of the BIRD-Bench Benchmark](https://mlsanity.com/paper/2402.12243/) (2024)
- [Coding Agents with Multimodal Browsing are Generalist Problem Solvers](https://mlsanity.com/paper/2506.03011/) (2025)
- [Agentar-Scale-SQL: Advancing Text-to-SQL through Orchestrated Test-Time Scaling](https://mlsanity.com/paper/2509.24403/) (2025)
- [DAComp: Benchmarking Data Agents across the Full Data Intelligence Lifecycle](https://mlsanity.com/paper/2512.04324/) (2025)
- [BIRD-INTERACT: Re-imagining Text-to-SQL Evaluation for Large Language Models via Lens of Dynamic Interactions](https://mlsanity.com/paper/2510.05318/) (2025)
- [AutoLink: Autonomous Schema Exploration and Expansion for Scalable Schema Linking in Text-to-SQL at Scale](https://mlsanity.com/paper/2511.17190/) (2025)
- [MARS-SQL: A multi-agent reinforcement learning framework for Text-to-SQL](https://mlsanity.com/paper/2511.01008/) (2025)
- [Enabling Self-Improving Agents to Learn at Test Time With Human-In-The-Loop Guidance](https://mlsanity.com/paper/2507.17131/) (2025)
- [AgentSM: Semantic Memory for Agentic Text-to-SQL](https://mlsanity.com/paper/2601.15709/) (2026)
- [Text-to-SQL as Dual-State Reasoning: Integrating Adaptive Context and Progressive Generation](https://mlsanity.com/paper/2511.21402/) (2025)
- [APEX-SQL: Talking to the data via Agentic Exploration for Text-to-SQL](https://mlsanity.com/paper/2602.16720/) (2026)
- [Leveraging Prior Experience: An Expandable Auxiliary Knowledge Base for Text-to-SQL](https://mlsanity.com/paper/2411.13244/) (2024)
- [NEMO: Execution-Aware Optimization Modeling via Autonomous Coding Agents](https://mlsanity.com/paper/2601.21372/) (2026)
- [RubikSQL: Lifelong Learning Agentic Knowledge Base as an Industrial NL2SQL System](https://mlsanity.com/paper/2508.17590/) (2025)
- [Memo-SQL: Structured Decomposition and Experience-Driven Self-Correction for Training-Free NL2SQL](https://mlsanity.com/paper/2601.10011/) (2026)
- [Learning to Retrieve: Dual-Level Long-Term Memory for Text-to-SQL Agents](https://mlsanity.com/paper/2606.00547/) (2026)

## Abstract (arXiv)

Production data integration is bottlenecked by repeated, lossy handoffs between data owners, engineers, and analysts who must collaboratively discover, structure, and query enterprise data. We present Data Intelligence Agents (DIA), a system of three agents (Data Interpreter, Schema Creator, and Query Generator) that compresses this workflow by treating autonomous coding agents (ACAs) as a first-class abstraction: rather than emitting text, the agents generate, execute, validate, and repair concrete artifacts, draw on a shared memory for experience reuse, and surface each for review by domain experts. DIA is deployed in production for enterprise customers. We study the Query Generator in depth and evaluate it in fully autonomous mode across seven SQL benchmarks spanning four task categories and four dialects. It matches or surpasses the best published results on all seven, demonstrating that an architecture grounded in execution, built on ACAs and a shared memory, generalizes across the data intelligence workload with adaptation confined to natural-language instructions.