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DataAgents: How we turned 9 months of analysis into 10 days
Capital One slashed cloud resource analysis time by over 95% by using an agentic pattern to automate Spark SQL generation and validation.
What: The 'DataAgent' framework coordinates AI-generated Spark SQL across 350 resource types to identify dormant cloud assets. The process includes automated false-positive checks and human-in-the-loop validation, turning a 9-month manual effort into 10 days.
Why it matters: This demonstrates a shift in enterprise IT: moving from 'manual analysis' to 'agentic workflows' where LLMs bridge the gap between asset metadata and query-ready infrastructure data.
Original article
Capital One's DataAgent pattern cut cloud dormancy analysis across about 350 AWS, Azure, and GCP resource types from 6-9 months to 10 days. It combines asset data, AI-generated Spark SQL, confidence scoring, false-positive checks, and human validation to find high-confidence savings opportunities.
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