SELF-HEALING DATA PIPELINES LEVERAGING AGENTIC AI FOR AUTONOMOUS MONITORING AND OPTIMIZATION IN AWS ENVIRONMENTS

Abstract

Modern data engineering demands resilient, self-sustaining pipeline architectures capable of adapting to failures without human intervention. This paper presents a comprehensive framework for self-healing data pipelines leveraging agentic AI within Amazon Web Services (AWS) environments. Traditional data pipelines remain vulnerable to cascading failures, schema drift, resource bottlenecks, and latency anomalies, resulting in significant operational overhead and data quality degradation. The proposed framework integrates agentic AI models capable of autonomous decision-making, enabling real-time monitoring, root cause analysis, and corrective action execution across distributed pipeline components. By harnessing AWS-native services including Amazon CloudWatch, AWS Lambda, AWS Glue, and Amazon EventBridge, the system continuously evaluates pipeline health metrics and autonomously triggers remediation workflows without manual intervention. Reinforcement learning and large language model-driven agents are employed to detect anomalies, predict failure patterns, and dynamically optimize resource allocation, throughput, and scheduling. Experimental evaluations demonstrate significant reductions in pipeline downtime, improved data quality scores, and enhanced operational efficiency compared to conventional rule-based monitoring approaches. The framework establishes a scalable, cost-effective paradigm for autonomous data infrastructure management, positioning agentic AI as a transformative enabler for next-generation cloud-native data engineering.