The terms wearable technology, wearable devices, and wearables all refer to electronic technologies or computers that are incorporated into items of clothing and accessories which can be worn comfortably on the body. Wearable technology usually provides sensory and scanning features not typically seen in mobile and laptop devices, such as biofeedback and tracking of physiological function. Thus, they could be profitably used in a number of applications, being important sources of data for reasoning. In particular wearables could be exploited in the design and implementation of a new breed of expert systems. An expert system is a computer system that emulates the decision-making ability of a human expert (Jackson, 1998). Expert systems are designed to solve complex problems by reasoning about knowledge, represented primarily as if-then rules rather than through conventional procedural code. A generic rule-based system consists of an inference engine and a knowledge base made of rules and facts to be analyzed. In the Knowledge Engineering (KE) field, expert systems development has been always conceived as an offline process, characterized by intense Knowledge Acquisition and Representation phases, conducted by the knowledge engineer in order to make explicit the knowledge involved in a decision making process, which is tacitly owned by human experts. This is not a simple task: as a matter of fact, the knowledge acquisition bottleneck (KAB) is a well-known and still unresolved problem (Gaines, 2013). This paper deals with development of wearable expert systems (WES). A WES is an expert system designed and implemented to obtain input from and give outputs to wearable devices. To interact with wearables, an expert system should be able to recognize how new data coming from them should be interpreted, in dynamically evolving scenarios. We assume that a WES features the following distinguishing characteristics with respect to traditional expert systems: – agile development cycle; – scalable and time-evolving knowledge bases; – interaction with a centralized, possibly cloud-based, knowledge maintenance system based on the knowledge artifact notion. Agility principles can be a starting point to solve the KAB problem, given that traditional expert systems development is not agile by definition, due to the need for three distinct roles: the user, the domain expert and the knowledge engineer. WES development, instead, will be centered on domain experts and users cooperating directly in the system development process, evaluating its effectiveness on the field and modifying and updating it quickly when necessary. Here we find the typical features of agile system development: the focus on incorporating user requirement changes during the project life-cycle (Lee and Xia, 2010) as well as rapidly creating and embracing changes, learning from them (Conboy, 2009).