Machine translation is increasingly used to support multilingual communication. Because of unavoidable translation errors, multilingual communication cannot accurately transfer information. We propose to shift from the transparentchannel metaphor to the human-interpreter (agent) metaphor. Instead of viewing machine translation mediated communication as a transparent channel, the interpreter (agent) encourages the dialog participants to collaborate, as their interactivity will be helpful in reducing the number of translation errors, the noise of the channel. We examine the translation issues raised by multilingual communication, and analyze the impact of interactivity on the elimination of translation errors. We propose an implementation of the agent metaphor, which promotes interactivity between dialog participants and the machine translator. We design the architecture of our agent, analyze the interaction process, describe decision support and autonomous behavior, and provide an example of repair strategy preparation. We conduct an English-Chinese communication task experiment on tangram arrangement. The experiment shows that, compared to the transparent-channel metaphor, our agent metaphor reduced human communication effort by 21.6%.