  Age of Information (AoI) has been recognized as an important metric to
measure the freshness of information. Central to this consensus is that
minimizing AoI can enhance the freshness of information, thereby facilitating
the accuracy of subsequent decision-making processes. However, to date the
direct causal relationship that links AoI to the utility of the decision-making
process is unexplored. To fill this gap, this paper provides a sampling-control
co-design problem, referred to as an age-aware remote Markov Decision Process
(MDP) problem, to explore this unexplored relationship. Our framework revisits
the sampling problem in [1] with a refined focus: moving from AoI penalty
minimization to directly optimizing goal-oriented remote decision-making
process under random delay. We derive that the age-aware remote MDP problem can
be reduced to a standard MDP problem without delays, and reveal that treating
AoI solely as a metric for optimization is not optimal in achieving remote
decision making. Instead, AoI can serve as important side information to
facilitate remote decision making.
