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

2026-06-17

AI Summary

Enterprise data integration often suffers from bottlenecks in data discovery, modeling, and querying due to inefficient handoffs among data owners, engineers, and analysts. This work proposes the first three-agent system centered on the Autonomous Coding Agent (ACA) abstraction, comprising a Data Interpreter, Schema Creator, and Query Generator. Leveraging an execution-driven architecture and a shared memory mechanism, the system enables end-to-end automated and auditable data workflows. It supports natural language–driven instructions, multi-dialect SQL generation, execution validation, and automatic repair. Evaluated across seven SQL benchmarks, the approach matches or surpasses state-of-the-art methods across four task categories and four SQL dialects, and has already been deployed in enterprise production environments.

Abstract

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.

Problem

• data integration
• enterprise data
• collaborative data discovery
• query generation
• workflow bottleneck

Innovation

• Autonomous Coding Agents
• Data Intelligence Agents
• Shared Memory
• Query Generation
• Enterprise Data Integration