Over the past couple of years, we’ve seen a remarkable surge in generative AI capabilities. Foundation models like LLMs can now understand context, identify insights, and generate content across various data types. But the real breakthrough is happening now, as we move from pure analysis to “agentic” action—intelligent workflows that can fix data issues automatically.
What are agentic workflows?
Agentic workflows rely on AI-powered agents equipped with LLMs to autonomously interact with data, applications, and APIs. They can plan, execute, and iterate on their actions using natural language commands, moving beyond detection to actual remediation. Unlike traditional rule-based systems, these agents can adapt dynamically, learn new patterns, and validate improvements without human intervention.
Solving Data Quality at Scale
Detecting Issues: Advanced profiling, ML models, and iterative LLM-based reasoning steps help identify complex data quality problems.
Fixing Issues: Instead of hard-coded rules, agentic workflows leverage LLMs to determine the right remediation approach—whether that’s validating addresses via an external API, confirming VAT codes, or performing document-based lookups.
Continuous Improvement: Agents can test and validate their fixes using multiple prompts and previously established data quality rules, constantly refining their approach.
Why It Matters
Better data quality means more reliable analytics, improved decision-making, and reduced operational costs. By embracing agentic workflows, organizations can turn today’s messy, error-prone datasets into trustworthy assets—fueling growth, innovation, and competitive advantage.
In short, the age of agentic workflows is here. It’s time to move from simply identifying what’s wrong with our data to taking action—automatically, intelligently, and at scale.
How is this being used in DQC’s DQ Platform
Within our DQ Platform, we’ve developed a modular framework that seamlessly integrates agentic workflows with our extensive library of connectors and data processing capabilities. This framework enables LLM-driven agents to orchestrate complex data quality operations at scale—profiling sources, tapping into public APIs for validation, leveraging internal knowledge repositories, and automatically orchestrating the right remediation actions. It’s designed to be easily expandable: adding new connectors, data types, or verification tasks can be done without rewriting entire decision logic. With this flexible, agentic architecture, we’re enabling autonomous, intelligent data quality improvements that continually evolve to meet the ever-changing demands of modern data ecosystems.
