  The extraction of a small number of relevant insights from vast amounts of
data is a crucial component of data-driven decision-making. However,
accomplishing this task requires considerable technical skills, domain
expertise, and human labor. This study explores the potential of using Large
Language Models (LLMs) to automate the discovery of insights in data,
leveraging recent advances in reasoning and code generation techniques. We
propose a new evaluation methodology based on a "capture the flag" principle,
measuring the ability of such models to recognize meaningful and pertinent
information (flags) in a dataset. We further propose two proof-of-concept
agents, with different inner workings, and compare their ability to capture
such flags in a real-world sales dataset. While the work reported here is
preliminary, our results are sufficiently interesting to mandate future
exploration by the community.
