Modernizing mission-critical systems is not simply a code-conversion problem. Enterprises must preserve business behavior, integration contracts, security controls, operational resilience, and decades of embedded domain knowledge while accelerating transformation.

This session presents a practical architecture for building an AI-driven software modernization factory for large and complex enterprise systems.

Drawing on experience with financial-services platforms, distributed architectures, and AI-assisted modernization, the session explores how Generative and Agentic AI can support the full modernization lifecycle: code understanding, dependency discovery, business-rule extraction, transformation, testing, behavioral validation, and production readiness.

Participants will learn practical patterns for:

analyzing and documenting large legacy codebases with AI;
extracting business logic, interfaces, dependencies, and operational behavior;
creating semantic representations that preserve system intent during transformation;
combining deterministic engineering controls with LLM-assisted code generation;
validating behavioral equivalence between legacy and modern implementations;
introducing human review, security, and policy gates into AI-generated changes;
integrating traceability, observability, testing, and quality controls into the modernization pipeline; and
scaling modernization across application portfolios without creating a new generation of technical debt.

A key theme is the transition from using AI as an individual coding assistant to treating AI as part of a governed software factory in which models, agents, engineering tools, testing systems, security controls, and human experts operate within a repeatable lifecycle.

Attendees will leave with a reference architecture and decision framework for applying AI to enterprise modernization while preserving the reliability, traceability, and architectural discipline required for critical systems.