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The AI-Native Codebase
Tue, 21 April
AI coding agents are now a default part of everyday software development, yet many teams struggle to use them reliably at scale. While AI can generate code quickly and in large volumes, that output often becomes difficult to review, understand, and maintain over time. As a result, adoption is frequently driven by trial and error rather than by deliberate design.
This session presents a five-level codebase maturity framework for creating and evolving codebases that support sustainable, production-quality development with AI coding agents. Each level defines clear goals, checklists, assessments, and success criteria, all grounded in real-world case studies. The talk explores how this framework leverages AI strengths such as speed and pattern recognition, while addressing weaknesses related to correctness, context loss, and long-term maintainability. The focus is on enabling effective human and AI collaboration so teams can ship reliable software at scale.
What You Wwill Learn
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A five-level maturity framework for assessing and evolving AI-ready codebases
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Practical criteria, checklists, and success measures for each maturity level
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How to balance AI-generated code with human oversight to maintain production quality
Who Should Attend
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Software Developers
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Software Architects
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Technical Leads and Engineering Managers
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Teams adopting or scaling AI-assisted development
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About the speaker
Ragunath Jawahar
Founder, Legacy Code HQ
Ragunath Jawahar is the Founder of Legacy Code HQ, where he specializes in helping developers and organizations master massive, complex codebases. With nearly 15 years in the industry and 5 years working with large codebases across startups and enterprises, he discovered that software complexity is fundamentally a human comprehension problem, not just a technical one.
To solve this challenge, Ragunath has built innovative visualization tools including Eureka and Timelapse (open-sourced on GitHub), which help developers navigate complex systems by surfacing relevant information while filtering out noise. His unique expertise combines legacy codebase rescue with 2+ years of AI-assisted development experience, positioning him to address a critical emerging problem: AI's acceleration of generating hard-to-maintain codebases.
Through his work at Legacy Code HQ, Ragunath teaches developers how to harness generative AI to build production-grade applications while avoiding maintainability pitfalls—leveraging first principles from human cognition, software development, and AI. This rare combination of legacy code mastery and AI expertise makes him uniquely qualified to help teams build maintainable software in the age of AI acceleration.
