A study on enhancing plan-execution flexibility in automated planning evaluates various deordering and reordering strategies, finding that block deordering-based approaches outperform MaxSAT-based methods due to their ability to change the causal structure of plans, thereby exposing new orderings and achieving higher execution flexibility. This research is relevant to AI agents as it explores techniques to improve the efficiency and flexibility of plan execution, which can be applied to various AI domains.