We typically believe that irony is a completely human affair, but there have been interesting attempts to create computational models of irony use and understanding. This chapter presents an overview of some of these models, especially as implemented as conversational agents. One of the beauties, and major challenges, of computer modeling is that it forces researchers to make concrete decisions on how best to implement some linguistic observation or theoretical idea (e.g., how to create a workable model of echoic mention, pretense, or what is meant by incongruity). Veale presents his EPIC model in which an expectation (E) predicts a property (P) of an instance (I) of concept (C) that can get upended by an ironic utterance. This model provides a quantifiable view of what it means for an ironic utterance to achieve its desired effect on an audience. The success of an ironic utterance hinges on its capacity to highlight the failure of a reasonable expectation. The effectiveness of this computational model was partly assessed by obtaining human judgments about the meaning and quality of different ironic utterances in varying contexts that are suggestive of different expectations. In this way, Veale’s work offers insights as to how engineering solutions may be very informative about the way irony functions in human communication.