The (PO)MDP framework is a standard model in planning and decision-making under uncertainty, but the complexity of its methods makes it impractical for any reasonably large problem. In addition, task-planning demands solutions satisfying efficiency and quality criteria, often unachievable through optimizing methods. We propose an approach to planning that postpones optimality in favor of faster, satisficing behavior, supported by context-sensitive assumptions that allow an agent to reduce the dimensionality of its decision problems.We argue that a practical problem solving agent may sometimes assume full observability and determinism, based on generalizations, domain knowledge and an attentional filter obtained through a formal understanding of “relevance”, therefore exploiting the structure of problems and not just their representations.