Four Common Loop Engineering Failures and How Developers Can Fix Them

Loop Engineering is an AI development approach where an agent iterates repeatedly toward a measurable goal instead of solving a problem manually. Developer Annie Wang identified four key failure modes in a recent discussion: runaway loops that drain token budgets, agents incorrectly self-evaluating their own outputs, goals too vague for a language model to act on, and tasks too complex for a single loop to handle. Runaway loops require hard stop rules to control costs, while self-grading agents should be replaced with a separate agent that independently checks another's work. Vague objectives like 'make this better' must be replaced with specific, non-negotiable criteria. When a single loop becomes overwhelmed by complexity, Wang recommends transitioning to Graph Engineering, a more structured multi-step architecture.