## 1 Introduction  
The usage of intelligent agents (IA) in everyday life has been increasing immensely. This includes the domains of medicine, military, traffic, and industry, while their use is widespread in many other fields as well. The U.S. military has keenly started to show its interest in artificial intelligence (AI) and augmented reality (AR) to improve their medical modeling and simulation (MMS) programs that train doctors, nurses, and first respondents in handling real-world situations. The MMS technology also helps military medical professionals face numerous medical concerns, whether in a military treatment facility or on the battleground [1]. Within the medical field includes the adaption of IA in the diagnosis of cardiac disorders [2]. Similarly, IA has also been used for the assessment of traffic conditions on highways [3]. In addition, refs. [4,5] describe the applications of IA in multiple types of apparel industries.  
IA, often referred as artificial agents, alludes to a self-ruling entity that evaluates a task like an individual, firm, machine, or program. It can also perform an assigned activity autonomously over some time using pieces of given relevant information. Though they may range from being simple to highly complex, they help keen operators learn or utilize information while accomplishing their objectives [6]. Some examples of such agents are Amazon Alexa, Apple Siri, Microsoft Cortana, Google Assistant, and robotic agents. Moreover, IA can be classified into four categories based on their capacity and level of accessibility [7].  
1. Simple Reflex Agents: These agents act based on the current inputs while ignoring past data. They work based on the condition-action rule, which relates a condition to action and works only when the environment is fully accessible.
2. Model-Based Reflex Agents: These agents keep track of the perception-history-based internal state and work even when the environment is partially accessible. The current state is stored within the agent and maintains an imprecise/predicted model of the environment's inaccessible sections.
3. Goal-Based Reflex Agents: An extension of Model-Based Reflex Agents, these agents make goal-based decisions. Each activity is intended to decrease the difference between the current state and the goal, thereby permitting the agent to pick the path leading to the goal from different potential decision paths/outcomes. The decision-making strategy can be modified, making these agents adaptive.
4. Utility-Based Agents: These agents are capable of recognizing both the goal and the system state. They are also capable of characterizing a proportion of how alluring the state is, i.e., the utility of a state. An objective utility-based agent picks the activity that boosts the utility of the activity results, i.e., what the agent hopes to infer, by and large, given the probabilities and utilities of every possible outcome.  
Synthetic Assistants (SA) are one such type of IA that are intended to give people physical help, either by participation or by performing tasks. They can be either software, hardware, or a blend of both that ideally supplements people's capacities in complex errands [8]. SA is most commonly used to aid people with physical disabilities at the workplace, such as monitoring those with impaired vision and facilitating physically demanding tasks. They exist within the scope of explicit models, yet they are regularly intended to be stylishly satisfying to make them attractive to users. There are many advancements in SA categories, such as VBSA, that can be applied to different applications in various emerging fields such as powered predictions, email, and navigation. In addition, there are also examples of failures in the real-life implementation of automated machines that potentially contribute to threats to human lives [4]. Thus, a careful collaboration between humans and machines is needed to overcome limitations and make efficient use of technology.