Why your AI product needs a different development lifecycle

AI products fundamentally break the assumptions of traditional software because they are inherently non-deterministic on both the input side (unpredictable user prompts) and output side (variable model responses).

Every AI product must negotiate a fundamental tradeoff between agency (the system's ability to act autonomously) and control (human oversight), and most teams fail by jumping to full agency before testing under high control.

The CC/CD (Continuous Calibration/Continuous Development) framework requires scoping AI products into versions defined by agency levels, starting with high-control/low-agency and gradually earning more autonomy.

Working with AI is like onboarding a new teammate: you should not hand them your highest-stakes projects on day one but instead start small, observe, build trust, and gradually expand their scope.

Never lead with the technology when building AI products; let the problem, evals, and data guide what gets added next, because too many people focus on chasing tools and frameworks and end up making costly mistakes.