Proficiency in problem-solving techniques is essential for engineers across various disciplines. Achieving a successful answer to a specific problem in sciences often necessitates a knowledge and understanding of aspects of the problem, and a corresponding optimal solution based on available problem-solving strategies. However, a need exists for a foundational framework, i.e., a basic model that incorporates generic visual elements within the realm of problem-solving. This model should avoid delving into unnecessary specifics of particular applications while addressing broader issues effectively. First, this paper comprehensively reviews current problem-solving theories to spot merits and gaps. Second, drawing inspiration from the idea of diversity, inclusion and equality in the natural organisation of humans, we address the crucial issue by conceptualising an understandable framework termed Diversification-Agentsourcing-Conversification, or abbreviated DAC theory. Our abstract model is derived from finding shared patterns of solutions in specific successful solvers. The components constituting the DAC model are: diversifier; agents; conversifier; and, their interconnections-plusprotocol, abbreviated interconnopol. The diversifier first parses a query problem among a set of networked intelligent agents; the agents, as partial decision-makers, play complementary roles. Afterwards, their responses are directed towards a conversification. The unique conversifier, taking into account all aspects, ultimately generates a solution to the problem through a frequently intricate and nonlinear process of agents decisions synthesis. We provide underlying theoretical facts and manifest how diverse principles and methodologies can be simply elucidated by our universal model. It is especially effective for complicated decision-making situations in the presence of uncertainty, where success is crucial. The key advantages of the DAC model are twofold: firstly, it demystifies existing solutions; and secondly, it plays a crucial role in transferring the knowledge acquired from the general solution pattern to effectively address new questions.