Key highlights
1,500+ lines
Processes highly complex SQL workloads at enterprise scale.
Up to six iterations
Uses automated self-healing validation and correction cycles.
Agentic AI
Automates conversion, validation, correction, and continuous learning.
A leading US-based regional bank needed to modernize legacy SQL assets for Snowflake and DBT. Manual migration was slow, difficult to scale, and error-prone. Zensar created an Agentic AI modernization framework that automated conversion, validation, error correction, and continuous learning, reducing engineering effort and improving migration consistency.
Client overview
Agentic AI-powered SQL-to-DBT modernization factory for Snowflake migration and governed data-platform transformation.
The bank was modernizing its enterprise data platform to improve scalability, analytics readiness, and operational efficiency. Large legacy SQL assets contained complex business logic and conversion dependencies. Traditional manual migration slowed delivery, while AI tools struggled with long scripts and failed conversions. Zensar implemented an Agentic AI framework that decomposes, converts, validates, corrects, and learns from each code-conversion cycle.
Zensar’s Brief - Steps taken by Zensar
Built a LangGraph-based Agentic AI workflow; automated SQL-to-Snowflake and SQL-to-DBT conversions; introduced intelligent code chunking and output stitching; enabled execution-based validation in Snowflake; added self-healing remediation loops; persisted new rules in Snowflake to improve future runs.
Beyond the Brief - How it helped the client
Reduced manual engineering effort, improved conversion quality and consistency, accelerated platform modernization, and created a reusable modernization factory that can be expanded to other SQL, Snowflake, and DBT transformation workloads.
Challenges
Scaling complex legacy SQL modernization for a regulated US financial institution moving toward Snowflake and DBT
The client needed to convert large volumes of legacy SQL logic into modern Snowflake and DBT artifacts. Many queries were 1,000-1,500 lines long and could not be reliably processed in a single AI call. Snowflake Cortex struggled with complex SQL, hit token limits, and failed conversions that required manual debugging. The lack of automation created delivery bottlenecks and increased transformation risk.
Solution
Agentic AI conversion framework that decomposes, converts, validates, self-heals, and continuously learns from modernization runs.
Zensar designed a multi-node Agentic AI pipeline powered by LangGraph and Claude models. The solution breaks large SQL queries into manageable segments, applies curated conversion rules, stitches the outputs together, executes the converted code in Snowflake, captures errors, and triggers up to six automated fix iterations. Newly discovered rules are stored in Snowflake, improving accuracy and repeatability over time.
Intelligent decomposition of large SQL into manageable chunks.
Automated SQL-to-Snowflake and SQL-to-DBT conversion.
Self-healing validation loop with error capture and retry.
Continuous-learning rule engine stored in Snowflake.
Solution enablers
LangGraph workflow orchestration.
Claude Sonnet AI reasoning and conversion.
Snowflake execution and rule repository.
Python automation and control framework.
Impact
Converted manual modernization work into a repeatable Agentic AI factory for faster, better Snowflake migration.
Automated code conversion
Handles 1,500+ line SQL workloads
Reduces manual debugging through self-healing
Delivers validated, production-ready output
Business outcome
The solution transformed a manual modernization program into an automated, repeatable migration model. By combining Agentic AI with Snowflake validation and continuous learning, the bank can accelerate modernization, reduce delivery risk, improve consistency, and build a scalable foundation for future data transformation initiatives.
Conclusion
This engagement demonstrates how Agentic AI can accelerate enterprise data modernization for highly regulated institutions. Zensar combined autonomous conversion, self-healing validation, and continuous learning to create a reusable framework that improves speed, quality, and repeatability while maximizing the value of Snowflake investments.