Intelligent Data Pipeline Failure Prevention: A Novel Framework Using AI Agents, RAG, and Vector Databases for Enhanced ETL Reliability

Abstract

Extract, Transform, Load (ETL) pipeline failures remain a critical challenge in modern data engineering, causing significant financial losses and operational disruptions. Traditional monitoring approaches are reactive and often fail to prevent catastrophic failures. This paper presents a novel framework leveraging Artificial Intelligence agents, Retrieval-Augmented Generation (RAG), Reflexion-RAG (REF-RAG), and vector databases to proactively predict, prevent, and remediate ETL pipeline failures. Our proposed system achieves 94.7% failure prediction accuracy (95% CI: 93.8%-95.6%) with a mean time to detection (MTTD) of 3.2 minutes (95% CI: 2.9-3.5 min), representing a 73% improvement (95% CI: 68%-78%) over conventional monitoring systems. Through comprehensive evaluation on production-scale datasets comprising 2.8 million pipeline executions spanning three diverse production environments, we demonstrate significant improvements in reliability, cost reduction, and automated remediation capabilities. The framework integrates multi-agent architectures with vector similarity search, enabling real-time anomaly detection and automated root cause analysis. Experimental results demonstrate a 68% reduction (95% CI: 64%-72%) in pipeline downtime and 82.4% automated remediation success rate (95% CI: 81.2%-83.6%).