Modernizing Data Quality with LLM‑Powered Autonomous Agents

With rapid growth and an expanding enterprise data ecosystem, the client sought to strengthen and scale their data quality management capabilities. They wanted to accelerate data quality rule creation and validation, reduce dependence on a limited pool of domain experts, and enable faster decision-making.
As new data sources were added and schemas evolved, the client also aimed to enhance the consistency of rule coverage across tables and layers. They needed a scalable approach that could systematically analyze data patterns, adapt to change, and support robust validation, while retaining human-in-the-loop governance and auditability.

Nagarro implemented an agentic AI framework designed to automate and orchestrate data quality management end-to-end.
The framework analyzes data patterns, business glossaries, schemas, and semantic models to discover and generate high-quality data rules, including cross-table and multi-layer validations.
All generated rules pass through a structured human-in-the-loop governance process to ensure business alignment and full audit traceability. Once approved, an Engineering Agent deploys the rules, reruns pipelines when required, and monitors for schema changes or new data sources, recommending additional rules as needed.
This coordinated, multi-agent approach enables continuous monitoring, adaptive rule generation, and controlled deployment, elevating reliability, consistency, and trust across the enterprise data landscape.

The framework significantly reduced the manual effort required to define and validate data quality rules, allowing teams to focus on higher-value analytical and strategic work.
Automated execution and intelligent orchestration accelerated deployment cycles, enabling business teams to access reliable insights more quickly.
With improved accuracy, consistency, and governance visibility, decision-makers now operate on a stronger foundation for reporting and analytics.
Because the system continuously adapts to new data sources and schema changes, it provides a scalable, future-ready platform that evolves alongside the organization’s growth.