AI-assisted development has dramatically increased software delivery speed, but it has also introduced a hidden liability: Comprehension Debt. Unlike technical debt, which results from poor implementation choices, comprehension debt accumulates when AI generates code, workflows, prompts, and integrations faster than engineers can understand, validate, or govern them. Over time, organizations lose visibility into why systems behave the way they do, making maintenance, security, compliance, and debugging increasingly difficult.

This session introduces Comprehension Debt as one of the defining engineering challenges of the AI era. We’ll explore why “”vibe coding”” succeeds for prototypes yet often fails in production, where systems must be explainable, secure, and maintainable. Through real enterprise examples, attendees will see how AI-generated artifacts create fragmented knowledge across developers, copilots, repositories, and business workflows, resulting in operational risk rather than business value.

The talk presents a practical engineering framework built around Data Foundation, AI Engineering, and AI Governance. Attendees will learn how architecture-first development, automated traceability, policy enforcement, and continuous validation can preserve developer velocity while ensuring every AI-generated change remains understandable, auditable, and production-ready.

Rather than arguing against AI coding assistants, this session demonstrates how organizations can embrace them responsibly—transforming AI from a code-generation tool into a disciplined engineering capability.
The audience will leave with concrete patterns for reducing comprehension debt, improving software resilience, and building AI systems that scale with confidence instead of complexity.