Typical solutions for agents assessing trust relies on the circulation of information on the individual level, i.e. reputational images, subjective experiences, statistical analysis, etc. This work presents an alternative approach, inspired to the cognitive heuristics enabling humans to reason at a categorial level. The approach is envisaged as a crucial ability for agents in order to:
(\emph{1}) estimate trustworthiness of unknown trustees based on an ascribed membership to categories;
(\emph{2}) learn a series of emergent relations between trustees observable properties and their effective abilities to fulfill tasks in situated conditions.
On such a basis, categorization is provided to recognize signs (\emph{Manifesta}) through which hidden capabilities (\emph{Kripta}) can be inferred. Learning is provided to refine reasoning attitudes needed to ascribe tasks to categories.
A series of architectures combining categorization abilities, individual experiences and context awareness are evaluated and compared in simulated experiments.