|Nowadays, Intelligent Personal Agents include Natural Language Understanding (NLU) modules, that utilize Machine Learning (ML), which can be included in different kind of applications in order to enable the translation of users' input into different kinds of actions, as well as ML modules that handle dialogue. This translation is attained by the matching of a user's sentence with an intent contained in an Agent. This paper introduces the first generation of the CERTH Intelligent Personal Agent (CIPA) which is based on the RASA framework and utilizes two machine learning models for NLU and dialogue flow classification. Besides the architecture of CIPA---Generation A, a novel dialogue-story generator that is based on the idea of adjacency pairs is introduced. By utilizing on this novel-generator, the agent is able to create all the possible dialog trees in order to handle conversations without training on existing data in contrast with the majority of the current alternative solutions. CIPA supports multiple intents and it is capable of classifying complex sentences consisting of two user's intents into two automatic operations from the part of the agent. The introduced CIPA---Generation A has been deployed and tested in a real-world scenario at Centre's of Research \& Technology Hellas (CERTH) nZEB Smart Home in two different domains, energy and health domain.|
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