Agentic Control Center for Data Product Optimization
🤖 AI Summary
This work addresses the high cost and limited scalability of manually constructing high-quality data products—such as question-answer pairs and database views—which traditionally rely on domain experts. To overcome these challenges, the authors propose an automated optimization framework based on a multi-agent system, introducing for the first time an agent control center architecture. This architecture continuously identifies user queries, monitors multidimensional quality metrics, and integrates a human-in-the-loop mechanism to ensure observability and iterative refinement of data assets. By maintaining human oversight while automating core optimization processes, the approach substantially reduces manual effort and significantly enhances the relevance, coverage, usability, and trustworthiness of data products.
Application Category
📝 Abstract
Data products enable end users to gain greater insights about their data by providing supporting assets, such as example question-SQL pairs which can be answered using the data or views over the database tables. However, producing useful data products is challenging, and typically requires domain experts to hand-craft supporting assets. We propose a system that automates data product improvement through specialized AI agents operating in a continuous optimization loop. By surfacing questions, monitoring multi-dimensional quality metrics, and supporting human-in-the-loop controls, it transforms data into observable and refinable assets that balance automation with trust and oversight.
Problem
Research questions and friction points this paper is trying to address.
data product optimization
supporting assets
automation
domain expertise
end-user insights
Innovation
Methods, ideas, or system contributions that make the work stand out.
Agentic AI
Data Product Optimization
Human-in-the-Loop
Continuous Optimization Loop
Automated Data Curation
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