When I joined the Entain engagement at Technogise, I was staring at a massive legacy distributed system with zero prior context. The codebase was sprawling, the documentation was sparse, and the business needed a critical feature delivered fast. Instead of spending weeks manually tracing code paths, I reached for Claude Code and Kiro CLI — not as shortcuts, but as force multipliers for the entire delivery lifecycle. What followed was a fundamentally different approach to legacy system delivery. I used Claude Code to deep-dive the codebase, rapidly mapping architecture, domain boundaries, and integration points that would have taken weeks to understand manually. Kiro CLI helped me plan implementation, generate scaffolding, and iterate on designs with a speed I hadn't experienced before. The result: end-to-end ownership of a business-critical feature, from comprehension to production. This post is a practitioner's account of that journey — what worked, what didn't, and how agentic AI tools are changing the way senior engineers approach legacy modernization. If you're a tech lead or senior engineer working on complex enterprise systems, this is the playbook I wish I'd had.

The Challenge: Zero Context, High Stakes

The Approach: AI-Augmented Codebase Comprehension

From Understanding to Implementation

Production and Beyond

Key Takeaways for Senior Engineers

Want to learn more about AI-assisted development? Connect with me on LinkedIn or check out my AI Research Agent Workshop.