This paper provides an overview of the development and demonstration of intelligent autonomy technologies for control of heterogeneous unmanned naval air and sea vehicles and describes some of the current limitations of such technologies. The focus is on modular technologies that support highly automated retasking and fully autonomous dynamic replanning for up to ten heterogeneous unmanned systems based on high-level mission objectives, priorities, constraints, and Rules-of-Engagement. A key aspect of the demonstrations is incorporating frequent naval operator evaluations in order to gain better understanding of the integrated man/machine system and its tactical utility. These evaluations help ensure that the automation can provide information to the user in a meaningful way and that the user has a sufficient level of control and situation awareness to task the system as needed to complete complex mission tasks. Another important aspect of the program is examination of the interactions of higher-level autonomy algorithms with other relevant components that would be needed within the decision-making and control loops. Examples of these are vision and other sensor processing algorithms, sensor fusion, obstacle avoidance, and other lower level vehicle autonomous navigation, guidance, and control functions. Initial experiments have been completed using medium and high-fidelity vehicle simulations in a virtual warfare environment and inexpensive surrogate vehicles in flight and in-water demonstrations. Simulation experiments included integration of multi-vehicle task allocation, dynamic replanning under constraints, lower level autonomous vehicle control, automatic assessment of the impact of contingencies on plans, management of situation awareness data, operator alert management, and a mixed-initiative operator interface. In-water demonstrations of a maritime situation awareness capability were completed in both a river and a harbor environment using unmanned surface vehicles and a buoy as surrogate platforms. In addition, a multiple heterogeneous vehicle demonstration was performed using five different types of small unmanned air and ground vehicles. This provided some initial experimentation with specifying tasking for high-level mission objectives and then mapping those objectives onto heterogeneous unmanned vehicles that each have different lower-level autonomy software. Finally, this paper will discuss lessons learned.