Building AI Agents for Data Quality

Data quality is one of those problems that never quite goes away. No matter how many rules you write, new edge cases emerge, and the maintenance burden grows. What if AI could help?

The Problem with Traditional DQ

Traditional data quality approaches rely on human-defined rules. Someone has to:

1. Analyze the data
2. Identify potential issues
3. Write validation rules
4. Maintain those rules as data evolves

This works, but it doesn't scale. As data volumes and sources grow, the manual effort becomes unsustainable.

Enter AI Agents

We've been building AI agents that can autonomously:

• Profile data to understand distributions and patterns
• Generate rules based on statistical analysis
• Detect anomalies that human-written rules might miss
• Suggest fixes for common data issues

Architecture Overview

Our DQ agent architecture consists of:

1. Data Profiler Agent: Continuously analyzes incoming data
2. Rule Generator Agent: Creates validation rules from patterns
3. Anomaly Detector Agent: Identifies unusual values or trends
4. Remediation Suggester Agent: Proposes fixes for issues

These agents communicate through a shared knowledge base and can be orchestrated to work together on complex DQ tasks.

Real-World Results

In a recent client engagement, our AI agents:

• Generated 200+ quality rules in hours (vs. weeks manually)
• Caught 15% more anomalies than the existing rule set
• Reduced DQ incident response time by 60%

Key Learnings

1. Start with clear objectives: AI needs direction
2. Human oversight matters: Review generated rules
3. Feedback loops improve results: Train on corrections
4. Integration is key: Connect with existing DQ tools

What's Next

The future is autonomous data quality—systems that not only detect issues but resolve them. We're working on agents that can automatically:

• Correct obvious data errors
• Enrich missing values from trusted sources
• Escalate complex issues with full context

Data quality doesn't have to be a constant battle. With AI agents, we can finally get ahead of the problem.