Research messy multi-format datasets autonomously

Poster gave an autonomous agent a messy dataset with mixed formats, missing values and conflicts, and reports an 80% cut in research time, though cost was a complaint.

Done with a custom-built or unnamed agent

What they did

The poster stress-tested an autonomous agent on a messy research dataset with multiple file formats, inconsistent labels, missing values, and conflicting entries. They asked it to clean and standardize the data, detect anomalies, produce a structured summary, and suggest next research steps; the poster says human review was still needed because the agent could be too confident with ambiguous data.

How it went

They report cutting a task that usually took 4–5 hours down to about 45 minutes, roughly an 80% time reduction, and say it surfaced patterns and inconsistencies they had missed. The stated drawback was occasional overconfident interpretation, and a commenter also called Manus very expensive.

Worth knowing

The clearest catch is cost: a commenter said Manus was ridiculously expensive, while the poster says you still need human validation for high-stakes analysis.

Source: Reddit · Undated