Technological advancement and increasing globalization makes humans face many problems in day to day life, involving many possible goals and each goal is associated with multiple possible actions, each associated with many different dynamic and uncertain consequences. In real systems, the message passing mechanisms and few computational intelligence techniques (like Swarm intelligence, Multiagent System, etc.) hinder mutual cooperation and coordination of agents while solving problems in an uncertain environment, even though they are highly efficient and sophisticated. Therefore, in this paper, we propose an Emergent Intelligence technique (EIT) based problem solving. The EIT is collective intelligence of group of agents, which is an extension of multiagent system (MAS). Unlike MAS, the EIT provides independent decision making for a single task by the multiple agents with mutual coordination and cooperation. It is very useful to solve the complex and dynamic problems in uncertain environments. In this paper, we discuss EIT functioning, benefits, comparisons, and also illustration of two problems: (1) resource allocation and (2) job scheduling. Each problem is categorically analyzed and solved step by step using EIT. We measure performance of the technique by considering real time situations, and results are compared and shown the importance of EIT over MAS. © 2019 by SCITEPRESS - Science and Technology Publications, Lda.