The first paper in a series of three introduces sequence‑prediction algorithms based on stringology. These methods use the size of the smallest straight‑line program and minimal automaton states to bound mistakes. Published by AI Alignment Forum, the work bridges agent foundations theory with practical algorithms. Practitioners can now test these efficient predictors on long symbolic sequences to improve learning speed.