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.
A practical enterprise AI architecture with data agents, AI-powered QA, and AI governance. The post Many Companies Use AI.
By Jiayan Yin
Giving an AI agent access to a data warehouse doesn't automatically make it agent-ready. The real challenge lies in teaching the agent what the data means and when it's reliable enough to use.
By Shafeeq Ur Rahaman
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.
By Anoushka Vyas, Aarushi Dhanuka, Sina Khoshfetrat Pakazad, Henrik Ohlsson
OpenAI’s new Data agent in ChatGPT Work lets users connect company data, uncover insights, and build interactive dashboards using natural language. The feature enables teams to put data to work more easily and interactively with AI assistance.
The article discusses transforming a demo LangGraph AI agent into a fully functional backend capable of handling real booking data. It outlines the steps and considerations involved in building a robust system that supports live data processing and integration. The focus is on practical implementation details rather than theoretical concepts.
By Soner Yıldırım