Integrating AI-Powered Data-Lineage Agents with Graph Databases for Enhanced Pipeline Transparency

This paper answers with intelligent data-lineage agents based on artificial intelligence methods and graph databases, offering a way to transparency and accelerating ETL pipeline analysis.The volumes and heterogeneity of orchestration tools increase they are what make for so many invisible points of failure late duplicating computations lost time and money this study is important because it tries to show how best to bring together the latest AI agents with graph databases towards more complete timely lineage tracing thus reducing incident localization time while building a dependable foundation for analytics as well as machine learning.This approach enables every metric to be instantly traced to its source data and code versions.The innovation of the method described in this paper lies in hybrid algorithms: few-shot prompting with large language models to extract the semantics of SQL and Spark code, utilizing graph neural networks to infer hidden relationships in a property graph, and reinforcement-learning methods based on expert feedback for enhancing data quality.The agent architecture comprises an event-subscription module conforming to the OpenLineage specification, a component for linguistic analysis of code, a machine inference layer that validates relationships, and a Neo4j graph database driver that supports versioning.The main findings are that the combination of AI agents and graph databases allows multi-hop lineage queries to run in fractions of a second, rather than requiring many join operations; automatically captures and updates dependency versions, with no costly migrations; preserves and protects the business semantics of each node; and a user-feedback loop that eventually minimizes the need for manual corrections.This article will be of value to data specialists, analytical platform architects, and IT project managers interested in enhancing the transparency, reliability, and regulatory compliance of modern ETL pipelines.