An Agentic AI Framework for Automating DataOps Monitoring and Analytics Workflows

Modern data pipelines are critical to organizational decision-making, yet they frequently suffer from issues such as data ingestion failures, schema mismatches, and data quality inconsistencies. Traditional monitoring systems rely heavily on manual intervention, leading to delayed issue resolution and reduced operational efficiency.

This project presents an agentic DataOps framework that leverages multiple intelligent agents to automate the detection, diagnosis, and analysis of data pipeline failures. The system integrates anomaly detection, schema validation, and natural language querying to enable real-time insights and automated debugging workflows.

Using real-world datasets, the proposed system demonstrates improved efficiency in identifying and resolving pipeline issues while reducing manual effort. The framework highlights the potential of agent-based architectures in transforming DataOps into a more scalable, autonomous, and intelligent process.