AI-SDLC creates a traceability problem that becomes expensive as products evolve. A product decision changes, while the delivery specifications, security requirements, test cases, and agent instructions built around its previous version remain unchanged.

Product Definition as Code is a recently proposed, open framework for tackling this problem. It keeps accepted product decisions versioned and connected, so delivery work, agent instructions, and checks can cite them instead of restating or reinterpreting them. This also reduces the repeated prose and supporting material that spec-driven workflows tend to accumulate.

We will follow one product rule from a versioned product definition into a delivery specification, GitHub Copilot instructions, security and quality requirements, and CI checks. Then we will change that rule and show how the workflow identifies consumers that are now relying on stale context.

The session combines product-definition traceability with secure AI-assisted engineering. It covers what can be checked mechanically, what coding agents can surface, and where human judgement remains essential.

Attendees will leave with a concrete way to accelerate AI-assisted delivery without letting outdated assumptions, product drift, or security gaps move silently into production.