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Cybersecurity Lead, PwC
“Very much looking forward to next year. I will be keeping my eye out for the date so I can make sure I lock it in my calendar.”
Software Engineering Specialist, Intuit
“Best conference I have ever been to with lots of insights and information on next generation technologies and those that are the need of the hour.”
Software Architect, GroupOn
“Happy to meet everyone who came from near and far. Glad to know you've discovered some great lessons here, and glad you joined us for all the discoveries great and small.”
Web Architect & Principal Engineer, Scott Davis
“Wonderful set of conferences, well organized, fantastic speakers, and an amazingly interactive set of audience. Thanks for having me at the events!”
Founder of Agile Developer Inc., Dr. Venkat Subramaniam
“What a buzz! The events have been instrumental in bringing the whole software community together. There has been something for everyone from developers to architects to business to vendors. Thanks everyone!”
Voltaire Yap, Global Events Manager, Oracle Corp.
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
A five-level maturity framework for assessing and evolving AI-ready codebases
Practical criteria, checklists, and success measures for each maturity level
How to balance AI-generated code with human oversight to maintain production quality
Who Should Attend
Software Developers
Software Architects
Technical Leads and Engineering Managers
Teams adopting or scaling AI-assisted development
