From Anomaly Detection to Agentic AI: Engineering Intelligent Data Reliability on Databricks

This story was originally published on HackerNoon at: https://hackernoon.com/from-anomaly-detection-to-agentic-ai-engineering-intelligent-data-reliability-on-databricks. Explore how Lokeshkumar Madabathula combines Databricks, real-time anomaly detection, lakehouse architecture, and governed Agentic AI for data reliability. Check more stories related to undefined at: https://hackernoon.com/c/undefined. You can also check exclusive content about #lokeshkumar-madabathula, #intelligent-data-operations, #real-time-anomaly-detection, #databricks-data-reliability, #delta-lake-architecture, #governed-enterprise-ai, #agentic-ai-for-data-engineers, #good-company, and more. This story was written by: @sanya_kapoor. Learn more about this writer by checking @sanya_kapoor's about page, and for more stories, please visit hackernoon.com. Lokeshkumar Madabathula explores how enterprises can move beyond reactive data monitoring by combining real-time anomaly detection, Databricks lakehouse architecture, and governed Agentic AI. His proposed framework uses Auto Loader, Structured Streaming, Delta Lake, Isolation Forest, and statistical thresholds to identify abnormal data behavior. An AI-assisted investigation layer then gathers operational context, develops root-cause hypotheses, assesses potential downstream impact, and recommends remediation, with security controls, audit trails, and human oversight governing consequential actions.