Context rot is a critical challenge where Large Language Model (LLM) performance significantly degrades as input length increases, contrary to the intuitive expectation of uniform context processing. It outlines empirical characteristics of this degradation, such as the detrimental impact of distractors and counter-intuitive effects of structural coherence, while also proposing immediate "context engineering" strategies and long-term research directions to mitigate the issue