In the realm of autonomous underwater exploration and surveillance, the capability of Autonomous Underwater Vehicles (AUV) to make informed decisions in dynamic and often unpredictable environments is crucial. This thesis investigates how a software architecture can be designed and implemented for autonomous decision making on the remotely operated vehicle BlueROV2. Using raw sensor data and external information inputs, situational awareness (SA) is achieved. This is used as a basis for real-time decision making during a search mission that is set up. The thesis aims for the AUV to base its actions on the SA. This is demonstrated through a search scenario in which the AUV is assigned the task of locating a number of targets in an underwater environment. To solve the task, the AUV is able to use two different methods to solve the same problem. Either by using the sonar to locate points of interest and subsequently investigate those locations or systematically traversing the entire search area using a camera to search. The trade-off between thoroughness and time consumption makes the methods favourable in different situations. Four modules are developed and implemented in the architecture. A sensor module that collects the data, an SA module that refines the data, a decision module that handles decisions in two layers and an execution module that performs the actions. A modular architecture is developed, which enables the AUV to decide which search method to prioritise and interpret the surroundings to differentiate between targets and decoy objects. Dynamic decision-making is accomplished by changing search strategies during missions and returning when all targets are found. The targets are located with a precision high enough to make out individual targets and keep track of previously seen targets. The research contributes to the field of autonomous decision making by laying a foundation for adding more sophisticated algorithms for future problem-solving. The implemented algorithms are placeholders to showcase the capabilities developed, and the architecture is modular to enable future changes and extensions of the work.