Building for AI Era: The ADLC Guide

AI agents fail differently from software — silently, plausibly, at scale — because they're probabilistic systems being

governed by a deterministic framework. SDLC's four foundational assumptions all break for agentic systems. ADLC

addresses the break with six phases organized into two loops: an inner loop that cycles during development until quality

thresholds are met, and an outer loop that cycles continuously in production. The context layer is the hidden variable that

determines whether ADLC actually works — it's the agent's operating environment, and without context lifecycle

discipline, even correctly implemented ADLC produces agents that degrade silently in production. For engineering

leaders, the path forward is drawing an explicit boundary between what SDLC governs and what ADLC governs,

assigning cross-functional ownership of the ADLC components, and adopting the framework in phases that match

organizational capacity. The organizations that do this now are building compounding advantages. The ones that wait are accumulating compounding risk.