Agentic AI for Mainframe Modernization: From COBOL Understanding to Automated Code Transformation

Mainframe modernization remains a complex challenge due to decades of accumulated business logic embedded in COBOL code. While generative AI has previously assisted developers with simple code explanations or conversions, the emergence of agentic AI offers a more comprehensive approach. Unlike standard AI assistants that respond to isolated prompts, agentic AI can pursue larger objectives by planning tasks, utilizing tools, and analyzing dependencies across an entire application ecosystem. This methodology shifts the focus from simple code translation to a structured process involving discovery, business rule extraction, impact analysis, and incremental refactoring. By integrating these agents into DevOps pipelines, organizations can better understand legacy systems, validate business logic, and perform safer, more effective transformations. Ultimately, this approach emphasizes that successful modernization is not just about changing languages, but about preserving valuable business knowledge while enabling more agile, hybrid architectures.