إطار جودة البيانات العملاقة يمنح التحقق مع التحقق من الإمكانية

43% of data scientists report that poor data quality is their biggest analytics bottleneck. The standard fix - static rule sets and manual curation - doesn't scale when every downstream task has its own definition of “good enough.” A new arXiv paper from an anonymous team (2606.13692) proposes an agentic-retrieval framework that treats data quality assessment as a context-aware, multi-agent problem, and it bakes in a feasibility gate that kills bad validation rules before they run.

Why Static Rules Fail for Data Quality

Data quality isn't absolute; a null-heavy column might be garbage for regression but fine for aggregation. Existing systems hard-code rules or rely on human SMEs to tune thresholds per use case. That doesn't scale when you have hundreds of datasets and dozens of consumption patterns.

This framework starts with a natural-language description of the intended data usage. A multi-agent workflow - powered by LLMs - parses that description, retrieves relevant schema and metadata, and derives a set of context-aware validation strategies. Each strategy gets translated into executable logic.

The Feasibility Check That Prevents Garbage Execution

Here's the concrete innovation: before any validation runs, a dedicated agent evaluates whether the generated specification is realistic and executable. If the logic references a column that doesn't exist, requires a join on a key with no match, or attempts a statistical test with insufficient data, the feasibility stage rejects it and triggers iterative refinement. Only accepted specs move to deterministic execution, ensuring results are reproducible and auditable.

The authors implemented a prototype and tested it across multiple usage scenarios on the same dataset. Outcomes adapted meaningfully per scenario - different quality scores for the same rows depending on intended use. Feasibility-gated execution reduced unrealistic or non-executable rule generation compared to a baseline that skipped that stage.

What This Means for Production Data Pipelines

Deploying LLMs to write validation logic is risky without guardrails. This framework gives data engineers a controlled path: the LLM suggests, the feasibility gate filters, and deterministic execution produces auditable results. The approach won't replace human judgment for edge cases, but it automates the 80% of quality checks that currently require manual configuration per dataset and use case.

Expect follow-up work to benchmark performance against specific rule sets on public data quality benchmarks like the NIST DQ framework or the UCI data repository. For now, the paper provides a practical architecture for anyone tired of writing the same null-check and range-validation rules dataset after dataset.

Source: An Agentic Retrieval Framework for Autonomous Context-Aware Data Quality Assessment
Domain: arxiv.org