Most explainable AI (XAI) research projects focus on well-delineated topics, such as interpretability of machine learning outcomes, knowledge sharing in a multi-agent system or human trust in agent’s performance. For the development of explanations in human-agent teams, a more integrative approach is needed. This paper proposes a perceptual-cognitive explanation (PeCoX) framework for the development of explanations that address both the perceptual and cognitive foundations of an agent’s behavior, distinguishing between explanation generation, communication and reception. It is a generic framework (i.e., the core is domain-agnostic and the perceptual layer is model-agnostic), and being developed and tested in the domains of transport, health-care and defense. The perceptual level entails the provision of an Intuitive Confidence Measure and the identification of the “foil” in a contrastive explanation. The cognitive level entails the selection of the beliefs, goals and emotions for explanations. Ontology Design Patterns are being constructed for the reasoning and communication, whereas Interaction Design Patterns are being constructed for the shaping of the multimodal communication. First results show (1) positive effects on human’s understanding of the perceptual and cognitive foundation of agent’s behavior, and (2) the need for harmonizing the explanations to the context and human’s information processing capabilities.