In this thesis, we focus on automated advising agents. The advice given is a form of relating recommendations or guidance from an automated agent to its human user. Providing the right advice at the right time is extremely complex, and requires a good adaptation to human desires and changing environments. We propose a novel methodology for designing automated advising agents and evaluate it in three real world environments. Our intelligent advising agents were evaluated through extensive field trials, with hundreds of human subjects. A significant increase in human performance as well as a high level of user satisfaction was recorded when they were equipped with our agents.