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Written by Max Zeshut
Founder at Agentmelt
A data integration pattern that extracts data from source systems, transforms it into a consistent format (cleaning, normalizing, enriching), and loads it into a destination (data warehouse, analytics platform, or AI model). AI data agents automate ETL by detecting schema changes, handling new data formats, resolving quality issues, and adapting transformation rules—tasks that traditionally require manual pipeline maintenance. Modern variations include ELT (load raw data first, transform in the warehouse) which AI agents also manage.
A data agent detects that a vendor changed their CSV export format—column names shifted and a new field was added. Instead of breaking the pipeline and requiring an engineer to fix it, the agent maps the new schema to the existing one, handles the new field, and logs the change for review.