The concept of an identifiable 'knowledge level' has proven to be important by shifting emphasis from purely representational issues to implementation-free descriptions of problem-solving. The knowledge level proposal enables retrospective analysis of existing problem-solving agents, but sheds little light on how theories of problem-solving can make predictive statements while remaining aloof from implementation details. In this report, we discuss the knowledge level architecture, a proposal which extends the concepts of Newell and which enables verifiable prediction. The only prerequisite for application of our approach is that a problem-solving agent must be decomposable to the cooperative actions of a number of more primitive subagents. Implications of our work are in two areas. First, at the practical level, our framework provides a means for guiding the development of AI systems which embody previously understood problem-solving methods. Second, at the foundations of AI level, our results provide a focal point about which a number of pivotal ideas of AI are merged to yield a new perspective on knowledge-based problem-solving. We conclude with a discussion of how our proposal relates to other threads of current research.