**Abstract** Recent developments have facilitated an increasing overlap between the research fields of Human-Machine Communication (HMC) and Computer-Mediated Communication (CMC). Both fields are dealing with increasingly autonomous agents (HMC) or channels (CMC), which are now sometimes the same: When intelligent voice-based agents (IVAs) present news, they simultaneously act as interlocutors and intermediaries, enabling direct and mediated communication. Hence, this study discusses and investigates empirically how interlocutor and intermediary predictors affect an assessment that is relevant for both: trustworthiness. We conducted a secondary analysis using data from two online surveys in which participants (N = 1288) had seven quasi-interactions with either Alexa or Google Assistant and calculated hierarchical regression analyses. Results show that (1) interlocutor and intermediary predictors influence people’s trustworthiness assessments when VBAs act as news presenters, and (2) that different trustworthiness dimensions are affected differently: The intermediary predictors (information credibility; company reputation) were more important for the cognition-based trustworthiness dimensions integrity and competence. In contrast, intermediary and interlocutor predictors (ontological classification; source attribution) were almost equally important for the affect-based trustworthiness dimension benevolence.