BrainBlog for Sage AI Technologies by Jason Bloomberg
Generative AI (genAI) is a legacy code modernization magic wand. Put that mainframe hairball in one end and out pops well-architected modern code out of the other, ready for deployment in the cloud of your choice, right?
Not so fast.
It’s true that there are now several genAI-based legacy code modernization tools on the market that can do a reasonable job of translating, say, COBOL or PL/I code into Java or Python, etc.
As a rule, however, these tools cannot scale. Feed them large codebases – the ‘hairballs’ that most mainframe-dependent organizations struggle with on a daily basis – and they fall over.
Sage AI Technologies, however, is the exception. Why does genAI fall short when modernizing such codebases? And how does Sage solve this hairball of a problem?
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Image credit: Craiyon.
