## 5 Distinguishing AI Agents in Autonomous Maritime Systems from Conventional AI Systems
### Human-AI Collaboration and Learning in Maritime Contexts  
In autonomous maritime systems, AI agents are not intended to function in isolation. Instead, they are embedded in operational ecosystems where humans remain critical participants, as supervisors, regulators, or emergency operators. Effective human-AI collaboration is therefore mandatory for ensuring safety, legal compliance, and public acceptance (Table 5). **Human Oversight and Collaboration**  
Maritime AI agents must support multiple forms of human oversight, including the following:  
1. Clear status reporting during mission execution;
2. Real-time alerts and explanations during uncertain or abnormal conditions;
3. Manual override interfaces that allow human operators to intervene when necessary [96,97,98,99]. This is especially critical in regulatory contexts or when ships traverse restricted or high-risk areas (e.g., straits, port approaches). Human operators must be able to understand the rationale behind the agent's decisions, not just observe the outcomes. Therefore, explainability and transparent decision logic are vital design requirements, far more stringent than in many conventional AI systems, where explainability is often optional or post hoc. Unlike conventional AI systems, maritime AI agents must be designed as collaborative partners, capable of working with humans and not merely replacing them. Learning and Adaptation in Dynamic Environments  
Autonomous maritime systems operate in fluid and unpredictable environments, where predefined rule sets or static models may quickly become obsolete. AI agents must therefore exhibit a high degree of adaptability and continuous learning. Examples include the following:  
Adjusting behavior in response to local weather or sea state changes;  
Learning new port approach procedures or traffic rules;  
Updating predictive maintenance models based on new equipment behavior or wear patterns [70,95]. Learning occurs through multiple layers, as follows:  
Onboard (edge) learning from local sensor and event data,  
Fleet-wide updates based on collective mission logs,  
Operator feedback loops where human judgment enhances or corrects system behavior. Unlike many conventional AI systems, which may operate in bounded, repetitive domains with infrequent updates, maritime AI agents must adapt autonomously and continuously, without compromising safety or interpretability. This ability to learn in real time, generalize across diverse conditions, and coordinate with humans is a defining characteristic of maritime AI agents. In conclusion, AI agents in maritime systems represent a highly specialized subclass of artificial intelligence, marked by unique design principles, operational constraints, and legal responsibilities. Their ability to make safe, compliant, and context-sensitive decisions under real-time pressure sets them apart from conventional AI systems. As autonomous shipping continues to evolve, these agents will play a central role in ensuring not just performance but also public trust, regulatory acceptance, and environmental accountability. Understanding their architecture, constraints, and distinguishing features is therefore essential for researchers, developers, and policymakers alike.