Explainable agency (XAg) aims at providing users with insights about the reasoning and decisions taken by an agent. Most of the newer XAg approaches are particularly useful as explanations for developers and researchers. In contrast, the novel XAg framework presented in this paper aims to address the explanation needs of end users, including both domain experts and lay users. It is a challenging task since this kind of users is not familiar with the methodological and technical aspects of agency. We propose a representation for end user questions and potential explanatory answers in both a verbal and a formal description as well as a mapping structure of questions to multiple possible explanations. We develop a pattern-based approach to extract explanatory content from an execution log and to validate potential answers to a user question which is based on the TriQPAN decision patterns from the literature [14]. We organize the novel concepts in a four-layered architecture with layers for end user questions, validation logic, TriQPAN patterns, and answer text generation. A running sample from a Jadex-BDI project on autonomous mobility on demand provides a demonstration scenario to illustrate some data structures and pseudocode. Further, it highlights the plausibility of our novel XAg framework.