Legacy modernization is not just about rewriting old code. It is about safely increasing the speed at which mission software can change.

Core Thesis -- Agentic AI does not replace the SDLC. It compresses, instruments, and stress-tests it.

The first principle is simple: legacy modernization fails when teams cannot safely understand, change, test, and release old systems fast enough. Agentic AI is valuable only when it improves one or more of those four functions:

This session shares lessons learned from more than 1,000 agentic-AI-assisted software release cycles, showing how AI agents can be embedded across the Software Development Lifecycle and Agile Development process frameworks to analyze legacy systems, census data schema, dependency map codebase, network map + visualization of functions, reduce technical debt, generate test suites, improve documentation, support refactoring, and accelerate [multi] cloud-readiness. Attendees will see a practical operating model for moving from AI coding experiments to governed AI-assisted [CI/CD] delivery pipelines.

The presentation will cover what has worked, what has failed, what has exceeded human performance, and what controls are required when agentic AI participates in real release workflows. Topics include agent roles, human-in-the-loop review, secure SDLC integration, modernization metrics, release gates, test automation, traceability, and risk management. The session is designed for leaders and practitioners responsible for modernizing aging mission systems while maintaining security, reliability, and operational trust.

Learning Objectives
By the end of the session, attendees will be able to:

1. Explain how agentic AI changes the modernization lifecycle from manual refactoring to a governed, AI-assisted delivery system.
2. Identify high-value SDLC use cases for AI agents, including legacy code analysis, requirements decomposition, test generation, documentation recovery, security review, and release readiness.
3. Recognize common failure modes in agentic software delivery, including context drift, hallucinated logic, weak test coverage, insecure automation, and over-trusting generated code.
4. Apply practical governance patterns such as human approval gates, traceability checks, policy-as-code, evaluation harnesses, and measurable release criteria.
5. Design a modernization roadmap that uses AI to reduce technical debt without creating unmanaged automation risk.