This paper introduces a preliminary agent-based model designed to simulate trust dynamics within a multi-agent scenario where virtual agents navigate unknown environments. The objective is to investigate how trust influences agent performance when they rely on an external source of information, referred to as a robot, versus relying on their peers. In the model, the robot explores the environment and reports the costs associated with different paths. Agents are categorized as either trustful or skeptical, deciding whether to trust the information provided by the robot or rely on their fellow agents. We evaluate three distinct scenarios: No Robot (baseline exploration), Immediate Start (simultaneous exploration by the robot and agents), and Early Start (robot-initiated exploration preceding agents’ involvement). Evaluation metrics include the number of agents successfully reaching the exit, exit times, and path costs. Our findings demonstrate significant advantages associated with the presence of the robot, particularly when exploration begins early. Trustful agents show improved performance by optimizing both the number of successful exits and the efficiency of path selection, resulting in reduced exit times and path costs.