Autonomy and adaptability are key features of intelligent agents. Many applications of intelligent agents, such as the control of ambient intelligence environments and autonomous intelligent robotic systems, require the processing of information coming in from many available sensors to produce adequate output responses in changing scenarios. Autonomy, in these cases, applies not only to the ability of the agent to produce correct outputs without human guidance, but also to its ubiquity and/or portability, low-power consumption and integrability. In this sense, an embedded electronic system implementation paradigm can be applied to the design of autonomous intelligent agents in order to satisfy the above mentioned characteristics. However, processing complex computational intelligence algorithms with tight delay constraints in resource-constrained and low power embedded systems is a challenging engineering problem. In this paper a single-chip intelligent agent based on a computationally efficient neuro-fuzzy information processing core is described. The system has been endowed with an information preprocessing module based on Principal Component Analysis (PCA) that permits a substantial reduction of the input space dimensionality with little loss of modeling capability. Moreover, the PCA module has been tested as a means to achieve deep adaptability in changing environment dynamics and to endow the agent with fault tolerance in the presence of sensor failures. For data driven trials and research, a data set obtained from an experimental intelligent inhabited environment has been used as a benchmark system.