From DataOps to AIOps: How autonomous agents are revolutionizing data engineering

Chakraborty, Soumen (2025) From DataOps to AIOps: How autonomous agents are revolutionizing data engineering. World Journal of Advanced Engineering Technology and Sciences, 15 (2). pp. 1403-1414. ISSN 2582-8266

This comprehensive article examines the paradigm shift from traditional DataOps to AI-powered DataOps (AIOps), highlighting how autonomous agents are fundamentally transforming data engineering practices. The evolution represents not merely a technological upgrade but a complete reimagining of data pipeline management—moving from human-centered operations to self-learning, autonomous systems. The article explores the core pillars of AIOps: automated observability that contextually understands metrics beyond simple collection, predictive issue resolution that anticipates and prevents problems before they impact operations, and AI-driven metadata management that creates comprehensive knowledge graphs. It introduces the agentic framework comprising horizontal agents (resource optimization, performance monitoring, cost management, and security) and vertical agents (data quality, governance, domain-specific, and lineage tracking) that collaborate to create a truly intelligent ecosystem. The article further examines self-healing pipelines and emerging trends, including LLM-powered conversational interfaces, self-optimizing pipelines, and generative AI for documentation, while providing a phased implementation roadmap for organizations beginning their AIOps journey.