The Framework Was Ready. The Adoption Wasn’t.
A leading custodial bank had built an internal no-code AI agent framework to let teams create intelligent agents without heavy engineering lift. The framework existed. Adoption didn't. Teams needed guidance to build agents that were effective, scalable, and compliant.
The data quality team had a separate problem. Business requirements came in as natural language. SQL rules had to go out. Nothing existed in between. Developers were translating requirements manually, request by request, with no end in sight.
The bank needed to solve both — and the solutions were more connected than they appeared.
Three Agents. One Architecture. Zero Manual Translation.
We worked with teams across the organization to drive confident framework adoption — providing documentation, hands-on guidance, and strategic direction on core agent types: personal avatars, RAG agents, and reasoning agents. Document-based knowledge integration and prompt engineering strategies ensured agents were built effectively and compliantly from the start.
For the data quality team, we designed and deployed three specialized agents:
- Rule Genie converts natural language prompts into rule-specific SQL by referencing an indexed repository of 17,000+ historical data quality rules. Developers validate. They no longer translate.
- SQL Genie converts natural language into standard SQL queries with explanations — helping developers prototype quickly and enabling business users who need SQL for reporting without query syntax expertise.
DQ Chat acts as the gateway — receiving user input, determining whether a rule-based or standard query is needed, routing to the right agent, and returning the result.
2,372 Rules in a Year. 3,200 in Five Months. 8,000 on Track.
In the 12 months before the agents launched, the firm created 2,372 data quality rules. In less than five months after launch, 3,200. The firm is on track to generate more than 8,000 in the first year — a 240% increase.
That's not just productivity. It's broader data coverage, faster compliance response, and stronger decision-making infrastructure built on rules that used to require a developer for every single one.
That's the difference between a framework that exists and one that gets used.