Cognitive impairment develops slowly, and overnight sleep changes years before a diagnosis is recorded. The George B. Moody PhysioNet Challenge 2026 asked participants to predict, from a single overnight polysomnogram (PSG), whether a patient later receives a diagnosis of mild cognitive impairment, Alzheimer's disease, or dementia. Our team (SleeperAgents) built a feature-based pipeline that reads each recording and its automated annotation file once and turns them into 218 features in nine blocks, covering demographics, whole-night time-domain statistics, annotation summaries, heart rate variability, band power and respiratory descriptors, stage-conditional cross-modal measures, first-order Markov sleep-stage dynamics, and stage-aware EEG biomarkers. A class conditional Markov log-likelihood ratio, cross-fit across recording sites, is appended as one extra column. An XGBoost classifier and a TabPFN v2 tabular foundation model are then trained on the resulting 219-column matrix, each calibrated with isotonic regression on leave-one-site-out out-of-fold predictions and blended with a weight tuned on the same predictions. Our entry received an age-conditioned AUROC of 0.604 on the official hidden validation set.