### Motivation  
The impetus behind the shift towards autonomous maritime transportation systems is multifaceted, encompassing both the imperative to enhance operational safety and the necessity to achieve sustainability goals. Traditional manned vessels, while effective, are inherently susceptible to human errors, which account for a significant proportion of maritime accidents [15; 16]. Factors such as fatigue, limited situational awareness, and delayed decision-making can compromise the safety of maritime operations [17; 18]. Autonomous systems, equipped with sophisticated AI agents, offer a solution by leveraging precise sensor data and advanced algorithms to perform tasks with higher accuracy and consistency than human operators [17; 19].  
AI agents contribute to safety by enabling autonomous vessels to process and interpret real-time data, facilitating proactive hazard detection and collision avoidance [20]. Machine learning algorithms can analyze patterns in sensor data to predict potential risks, allowing vessels to navigate safely even in challenging conditions [21]. For example, AI-driven collision avoidance systems can calculate optimal evasive maneuvers in real time, significantly reducing the likelihood of accidents caused by human error or delayed reactions [22; 23].  
In parallel, the maritime industry faces mounting regulatory pressures to reduce its environmental footprint. The IMO's regulatory framework, including the CII, aims to curb greenhouse gas emissions from shipping by promoting energy efficiency and the adoption of cleaner technologies [24]. The EU ITS further reinforces these objectives by integrating intelligent transport systems to enhance the efficiency and sustainability of maritime logistics [25]. Compliance with these regulations necessitates the adoption of technologies that can monitor and optimize vessel performance in real time, ensuring adherence to emission standards while maintaining operational efficiency [26].  
AI agents are instrumental in this context, as they can dynamically adjust vessel operations to optimize fuel consumption and reduce emissions. By analyzing data related to engine performance, fuel usage, and environmental conditions, AI systems can recommend adjustments to speed, routing, and power settings to achieve optimal performance. This real-time optimization not only ensures regulatory compliance but also contributes to cost savings and a reduction in the maritime sector's overall carbon footprint.  
Furthermore, the integration of AI into maritime systems opens avenues for enhanced situational awareness and predictive maintenance. AI-driven predictive analytics can forecast equipment failures before they occur, enabling preemptive maintenance actions that prevent breakdowns and extend the lifespan of critical maritime infrastructure. Enhanced situational awareness, achieved through the fusion of data from multiple sensors, allows autonomous vessels to navigate more effectively in congested or adverse conditions, thereby increasing the overall resilience of maritime operations.  
The convergence of these factors--improving safety, achieving sustainability, and complying with stringent regulations--creates a compelling motivation for the widespread adoption of autonomous maritime systems. AI agents, with their ability to process vast amounts of data in real time and make informed decisions, are at the heart of this transformation, driving the maritime industry towards a safer, more efficient, and environmentally responsible future.