Data quality assessment is a critical prerequisite for effective data analytics and data-driven decision-making, yet it remains a challenging task due to the inherently context-dependent nature of data quality. Existing approaches often rely on static rules or manual assessment strategies, limiting their adaptability to diverse usage scenarios and constraining automation at scale. Recent advances in artificial intelligence, particularly large language models, offer new opportunities for automating data quality assessment, but raise concerns related to reliability, grounding, and execution safety. In this paper, we propose a unified agentic-retrieval framework for autonomous context-aware data quality assessment. The framework interprets natural-language descriptions of intended data usage, derives context-aware assessment strategies, and generates executable validation logic through a multi-agent workflow. To ensure operational reliability, the framework introduces a feasibility validation stage that evaluates the realism and executability of generated assessment specifications before execution, enabling iterative refinement when necessary. Accepted validation logic is executed deterministically to guarantee reproducible and auditable results. We implement the proposed framework as an end-to-end prototype and evaluate it across multiple usage scenarios applied to the same dataset. The results demonstrate that assessment outcomes adapt meaningfully to different intended uses, while feasibility-gated execution reduces unrealistic or non-executable rule generation. The proposed approach provides a practical foundation for deploying autonomous yet controlled data quality assessment in modern data-driven environments.

翻译：数据质量评估是有效数据分析和数据驱动决策的关键前提，但由于数据质量本质上依赖于上下文，这仍然是一项具有挑战性的任务。现有方法通常依赖于静态规则或人工评估策略，限制了其在不同使用场景中的适应性，并制约了大规模自动化。人工智能的最新进展，特别是大语言模型，为自动化数据质量评估提供了新的机遇，但也引发了有关可靠性、基础依据和执行安全性的担忧。本文提出了一个统一的自主检索框架，用于上下文感知的数据质量自动评估。该框架能够解释预期数据用途的自然语言描述，推导出上下文感知的评估策略，并通过多智能体工作流生成可执行的验证逻辑。为确保运行可靠性，框架引入了一个可行性验证阶段，在生成评估规范后、执行前，评估其真实性和可执行性，必要时进行迭代优化。被接受的验证逻辑会被确定性地执行，以保证结果的可复现性和可审计性。我们将所提出的框架实现为一个端到端原型，并在同一数据集上针对多个使用场景进行了评估。结果表明，评估结果会随不同的预期用途而自适应地变化，同时，基于可行性门控的执行减少了不切实际或不可执行规则的生成。所提出的方法为在现代数据驱动环境中部署自主可控的数据质量评估提供了实用基础。