This paper presents a new approach to the analysis and design of intelligent tutoring systems (ITS), based on reactive principles and cognitive models, this way leading to multiagent architecture. In these kinds of models, the analysis problem is treated bottom-up, as opposed to that of traditional artificial intelligence (AI), i.e., top down. We present one ITS example called Makatsina (meaning tutor in TOTONACA, a Mexican pre-Columbian language), constructed according to this approach, which teaches the skills necessary to solve the truss analysis problem by the method of joints. This learning domain is an integration skill. The classical ITS work is based on explicit goals and an internal representation of the environment. The new approach has reactive agents which have no representation of their environment and act using a stimulus response behavior type. In this way they can respond to the present state of the environment in which they are embedded. With these elements, errors, and teaching plans, each agent behaves as an expert assistant that is able to handle different teaching methods. Reactive agent programming is found to be simple because agents have simple behaviors. The difficulty lies in the interaction mechanism analysis and design between the environment and the intelligent reactive system.