Intelligent Data Pipeline Failure Prevention: A Novel Framework Using AI Agents, RAG, and Vector Databases for Enhanced ETL Reliability
DOI:
https://doi.org/10.70153/IJCMI/2025.17304Keywords:
ETL Pipelines, AI Agents, RAG, Vector Databases, Failure PreventionAbstract
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%).
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