Achieving climate neutrality will require a major transformation of the transportation sector, likely leading to a surge in demand for electric vehicles (EVs). This poses a challenge to grid stability due to supply fluctuations of renewable energy resources. At the same time, EVs offer the potential to improve grid stability through managed charging. The complexity of this charging process can limit user flexibility and require more cognitive effort. Smart charging agents powered by artificial intelligence (AI) can address these challenges by optimizing charging profiles based on grid load predictions, but users must trust such systems to attain collective goals in a collaborative manner. In this study, we focus on traceability as a prerequisite for understanding and predicting system behavior and trust calibration. Subjective information processing awareness (SIPA) differentiates traceability into transparency, understandability, and predictability. The study aims to investigate the relationship between traceability, trust, and prediction performance in the context of smart charging agents through an online experiment. N = 57 participants repeatedly observed cost calculations made by a schematic algorithm, while the amount of disclosed information that formed the basis of the cost calculations was varied. Results showed that higher amount of disclosed information was related to higher reported trust. Moreover, traceability was partially higher in the high-information group than the medium and low-information groups. Conversely, participants’ performance in estimating the booking costs did not vary with amount of disclosed information. This pattern of results might reflect an explainability pitfall: Users of smart charging agents might trust these systems more as traceability increases, regardless of how well they understand the system.