AI Agentic Architectures for Autonomous Data Engineering Pipelines
TL;DR
This study delves into the notion of AI agentic architectures for autonomous data engineering pipelines and investigates the potential benefits of intelligent agents in enhancing automation, resilience, and decision-making processes in contemporary data ecosystems.
Abstract
Tremendous advances have been made in big data technologies, cloud computing, and artificial intelligence, which have dramatically changed the way engineers work today. The limitations of traditional data engineering pipelines are manual dependency, static workflow orchestration, and rule-based monitoring mechanisms, restricting scalability, flexibility, and efficiency in dynamic computing environments. With the processing of large amounts of structured and unstructured data, there is a growing need for intelligent and autonomous data processing systems that require minimal human involvement in data pipeline management. This study delves into the notion of AI agentic architectures for autonomous data engineering pipelines and investigates the potential benefits of intelligent agents in enhancing automation, resilience, and decision-making processes in contemporary data ecosystems The research is focused on the design and evaluation of an Agent-based architecture that could be capable of performing critical data engineering tasks (data ingestion, data validation, data orchestration, data monitoring, data optimization, and fault recovery) autonomously. The study is conducted with a hybrid method combining architectural modeling, comparison, and evaluation through simulation with current AI and cloud technologies. The suggested framework incorporates autonomous AI agents, machine learning algorithms, and intelligent orchestration mechanisms to facilitate adaptive and self-healing operations of pipelines
