T-Bank has overhauled its data quality management processes, transitioning from a manual workflow to an automated system powered by AI agents. The previous approach required 11 lengthy manual steps to ensure data integrity, which became unsustainable as the volume of data grew.
The company opted for an agent-based architecture over a low-code solution or a simple chain of prompts. This new system incorporates intent recognition to identify and resolve data issues before they impact downstream processes, which is critical for the bank's AI-driven operations.
The development team focused on observability and operational efficiency, noting that reliable data is a prerequisite for AI agents. By automating these checks, the bank aims to prevent errors from propagating through its data infrastructure.
