Aws Re:invent 2025 - Driving Modernization Using Mphasis’ Agentic...
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📖 AWS re:Invent 2025 - Driving modernization using Mphasis’ Agentic AI framework (MAM219)
In this video, Anup Nair (CTO) and Bharat (Senior Partner) from Mphasis.ai present their Agentic AI Framework for legacy modernization. They address the common CIO challenge of being unable to innovate due to risky legacy systems built on COBOL, Natural, and Adabas. Their solution uses four autonomous agents: NeoZeta (extracts intelligence from code into knowledge graphs), NeoSaba (generates user stories with INVEST scoring), NeoRena (defines target architecture), and NeoCrux (generates code). The framework centers on Ontosphere, an enterprise knowledge graph with domain ontologies. Live demos show reverse engineering of post-trade processing COBOL programs with 95% accuracy using LLM as a Judge, GPU vs CPU performance comparisons, and BDD generation. Results show 50 million lines of code modernized in 18 months versus traditional 7 years, with intelligence converted to data to eliminate future legacy issues.
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My name is Anup Nair. I'm the CTO for Mphasis.ai, and I'm Bharat, Senior Partner in Mphasis.ai. We are going to talk about legacy modernization and Mphasis' Agentic AI Framework for legacy modernization. That's what we're going to discuss today. We have some exciting demos as well as part of this presentation, so I'm hoping you'll like it. If you have any questions, please feel free to get us offline. I don't think they allow us questions here, but yeah, why not, right?
To start off with, let me give you a little background here. Let's discuss the problem statement first. In the last 25 years, every CIO I have met has had this problem: I can't innovate fast enough because it's too risky to touch any legacy platform. Are you guys on the same page when it comes to this? In fact, one of the CIOs actually told me that I have 49 core systems that are built on COBOL mainframes, and if I touch one of them, I have to touch all the remaining 48 as well. So this is the problem we decided to solve.
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Source: Dev.to
