When humans and remote robots work together on a team, the robots always interact with a human supervisor, even if the interaction is limited to occasional reports. Distracting a human with robotic interactions doesn't pose a problem so long as the inclusion of robots increases the team's overall effectiveness. Unfortunately, increasing the supervisor's cognitive load may decrease the team's sustainable performance to the point where robotic agents are more a liability than an asset. Present approaches resolve this problem with adaptive autonomy, where a robot changes its level of autonomy based on the supervisor's cognitive load. This thesis proposes to augment adaptive autonomy by modeling temporal latency and using this model to optimally select the temporal interval between when a supervisor is informed of a pending change and when the robot makes the change. This enables robotic team members to time their actions in response to the supervisor's cognitive load. The hypothesis is confirmed in a user-study where 26 participants interacted with a simulated search-and-rescue scenario.