A Comprehensive Review of Agentic Artificial Intelligence for Autonomous Data Pipeline Management: Advances, Challenges, and Future Directions

The rapid development of cloud technology, big data, and artificial intelligence has changed the way data pipelines are managed in today’s world. Conventional data pipeline management systems depend on rule-based automation and manual intervention making them less effective in managing dynamic workloads, detecting anomalies, recovering from faults, and optimizing resources. The recent advances in Agentic Artificial Intelligence, LLMs, reinforcement learning, and multi-agent systems have given rise to the autonomous systems that can perform monitoring, diagnosis, recovery, and optimization on their own. This review studies the state-of-the-art AI information pipeline management processes, intelligent work flow orchestration, autonomous decision making, and self-healing systems. The paper reviews the existing methodologies, including ETL automation, metadata-based data ingestion, optimization through reinforcement learning, and agent-based orchestration systems, and presents their advantages, limitations, and potential fields of application. Moreover, the review identifies main research problems faced by researchers and developers including explainability, interoperability, scalability, security, and adaptation in heterogeneous cloud environments.