In this article, we discuss the evolution of Meta’s data warehouse to enhance productivity and security for both human users and AI agents. We are developing specialized agents to facilitate data access requests and assist data owners in managing permissions securely. By using guardrails like auditing and feedback systems, we aim to keep agent operations within defined boundaries.
Given the increasing scale and complexity of data access patterns fueled by GenAI, a new agentic workflow has been implemented. This multi-agent system separates data-user and data-owner functionalities to streamline request processing. Key features include context-aware data exploration, query-level access control, and a dynamic intention management system to optimize data access based on user needs.
Collaboration among agents and continuous evaluation through real-time feedback is essential for managing the complexities of data distribution. Our focus is on creating a secure, efficient environment that supports both AI-driven and human-driven data analytics initiatives.
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