Case-based reasoning (CBR) solves new problems by retrieving solutions to similar prior problems and adapting them to fit new needs. Progress in retrieval methods for CBR has resulted in a flourishing technology of case-based "aiding systems" that support human problem-solving by automatically providing the user with relevant cases. However, developing effective methods for automated case adaptation remains a central research challenge for the field. This article proposes alleviating the adaptation problem by using a case-based adaptation component to capture and reuse the reasoning underlying successful adaptations. More generally, it illustrates the potential for case-based intelligent components within CBR systems, presents specific principles and observations concerning their application to autonomous and interactive case adaptation, and suggests their potential role in improving similarity assessment and case retrieval. In addition, it proposes that adaptation learning helps to address the increasingly important problem of case-base maintenance, by enabling a "lazy updating" of the case base as new knowledge is acquired.