Tables Turned: Why LLM-Based Table Agents Are the Next Big Leap in Business AI
TL;DR for operators Most business data does not live in pristine chatbot-friendly prose. It lives in spreadsheets, ledgers, CSV exports, relational databases, dashboards, compliance reports, and those heroic Excel files with merged cells, colour-coded warnings, unexplained abbreviations, and one column called misc. The paper behind this article, Toward Real-World Table Agents, argues that LLM-based table agents should not be judged as smarter versions of Text-to-SQL alone.1 Real-world table work requires an end-to-end workflow: reading table structure, cleaning noisy semantics, retrieving only the relevant parts, executing traceable reasoning steps, and adapting to domains such as finance, healthcare, public administration, and industrial operations. ...