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.
arXiv:2606. 19319v1 Announce Type: cross 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.
arXiv:2508. 05002v2 Announce Type: replace-cross Abstract: Existing unstructured data analytics systems rely on experts to write code and manage complex analysis workflows, making them both expensive and time-consuming.
arXiv:2608. 09532v1 Announce Type: cross Abstract: Enterprises increasingly seek to query data lakes using natural language via AI-driven tools like semantic operators or deep research agents.
arXiv:2609.24137v1 Announce Type: cross Abstract: Traditional data systems face profound limitations in the AI era, relying on human-crafted pipelines, lacking semantic understanding of heterogeneous...
Traditional data systems face profound limitations in the AI era, relying on human-crafted pipelines, lacking semantic understanding of heterogeneous data, and operating through rigid, reactive proces...
The paper introduces the DevRev NL2SQL benchmark, comprising 900 execution‑verified queries that feature nested‑type and link‑graph structures, along with the Semantic Depth Score (SDS) to assess analytical reasoning depth. It also presents a cost‑aware single‑generation agentic architecture that includes schema selection, metadata retrieval, and error‑repair components tailored to nested enterprise schemas. On the DevRev benchmark, the system achieves 91.7% answer correctness, outperforming the next‑best baseline by 54.6 percentage points, and remains competitive on the Spider 2.0 Snowflake dataset.
arXiv:2608. 14246v1 Announce Type: new Abstract: In today's fast-paced environment, the ability to swiftly access, understand, and act on data is no longer optional; it is essential.
arXiv:2605. 21027v2 Announce Type: replace-cross Abstract: Enterprise analytics aims to make organizational data accessible for decision-making, yet non-technical users still face barriers when using traditional business intelligence tools or Text-to-SQL systems.
The paper introduces the DevRev NL2SQL benchmark, featuring 900 execution‑verified queries that test natural‑language‑to‑SQL systems on nested, graph‑like enterprise schemas, and proposes the Semantic Depth Score (SDS) as a rubric for analytical reasoning depth. It also presents a cost‑aware, single‑generation agentic architecture that includes schema selection, metadata retrieval, and error‑repair components tailored to these complex schemas. On the DevRev benchmark, the system achieves 91.7% answer correctness, outperforming the next‑best baseline by 54.6 percentage points, and remains competitive on the Spider 2.0 Snowflake dataset.
arXiv:2607. 06229v1 Announce Type: cross Abstract: Major cloud data platforms now expose large language model capabilities as native SQL functions, enabling analysts to perform classification, filtering, sentiment analysis, extraction, similarity search, and aggregation within ordinary SQL queries.
EvoOntology introduces a self‑evolving ontology layer for data agents, encapsulating the ontology as an MCP server with schema, content, and tool layers. It enables agents to query and interact with the ontology at runtime, using a builder agent for autonomous construction and a self‑evolution loop that refines the ontology through attribution‑guided edits validated by backbone‑conditional evaluation. Experiments on three data‑agent benchmarks with four LLM backbones show that EvoOntology consistently outperforms strong baselines and existing semantic‑layer approaches, effectively bridging the agent‑data gap for heterogeneous data.
arXiv:2607. 25042v1 Announce Type: new Abstract: The evolution of customer support systems is rapidly advancing with agentic chatbots, yet these systems face significant limitations when accessing enterprise data without predefined API endpoints.