Enterprise Analytics

TL;DR for operators A data agent can find the right evidence, perform much of the required analysis, and still fail the task by returning the wrong table. In an audit of 136 failed runs from the strongest tested backbone, 71 failures—52.2%—were attributed primarily to turning the agent’s internal result into the requested output. Sixty of those involved submitting extra or missing columns. Only three failures were attributed to selecting the wrong evidence source. ...

TL;DR for operators The paper introduces an “AI Data Scientist”: a six-subagent system that moves from raw tabular data to cleaned data, tested hypotheses, engineered features, trained models, and business-facing recommendations.1 The useful idea is not that another agent can write Python. Congratulations, we have met 2025. The useful idea is that hypothesis testing becomes the workflow’s organising rail. ...