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Conversational agents are a promising way to provide assistance to novice users. After a semantic analysis, natural language requests are transformed into a formal representation the agent is using in conjunction with a model of the application to define the most appropriated reaction. But heuristics associating behaviors to patterns of semantically similar requests often fail to provide a reaction both efficient and realistic when they are only based on purely rational decisions. Therefore, we propose here an architecture for assisting conversational agents based on two notions: heuristics taking into account both rational and subjective parameters (based on a psychological model of the agent), and biases used to model deep personality contraints the agent can't modify (implemented as modifiers over the messages transmitted by the agent). We illustrate its functioning with typical requests extracted from a corpus of requests to an assisting agent.