Autonomous Data Agent — Self-Managing ETL & Analysis Pipeline

I will build an autonomous data processing agent that independently handles data ingestion, quality assessment, cleaning decisions, transformation, and analysis — adapting its processing approach based on data characteristics it discovers rather than following a fixed pipeline that breaks on unexpected data formats or quality issues. Fixed ETL pipelines fail silently or loudly on unexpected data — autonomous agents that evaluate incoming data quality and adapt processing accordingly are more robust in environments where data format, completeness, and quality vary across sources and time periods.

The agent covers autonomous data quality assessment, adaptive cleaning strategy selection based on quality findings, transformation decision-making, analysis depth calibration based on data richness, exception handling for genuinely unprocessable data, and a processing report documenting all decisions made.