Managing Vector Data for Retrieval Augmented Generation@Dagstuhl Seminar Summary

Abstract

This report summarizes discussions that took place in the context of a Dagstuhl seminar1 to explore the emerging paradigm of AI-native databases designed to support agentic workflows and Retrieval-Augmented Generation (RAG) by integrating Large Language Models (LLMs) with robust database abstractions. The central synthesis of the Dagstuhl discussions is the necessary evolution of databases from passive vector stores into active data-native agentic harnesses that orchestrate AI reasoning and task-centric execution. A recurring lesson across keynotes and working groups is that achieving mission-critical reliability requires integrating AI primitives, such as embeddings, model inference, and semantic operators, as first-class citizens while maintaining traditional database guarantees like transactional consistency, explainability, and cost-aware optimization. This transition necessitates a structured approach to agent memory (spanning episodic, semantic, and procedural scales) and a shift toward adaptive, iterative execution models that go beyond static query planning to accommodate the stochastic nature of foundation models. There is a community consensus that reliability, responsibility, and security (specifically addressing compositional leakage) must be treated as end-to-end concerns supported by holistic benchmarks that evaluate the entire quality chain from retrieval to autonomous action.