Although several methodologies, processes, and frameworks are available for constructing sophisticated autonomous multiagent systems organizations, none of them provide techniques for the reliability analysis of multiagent systems designs. This is an important issue when designing a multiagent system because of the nature of the environments where it operates (dynamic, continuous, and partially accessible). Additionally, the multiagent system must be adaptive (self-organized) to adjust its behavior to cope with the dynamic appearance and disappearance of goals (tasks), their given guidelines, and the overall goal of the multiagent system. To address such an issue, we propose a novel approach for computing the reliability, in design time, of organization-based multiagent systems. This process consists of five steps. First, the multi-agent system is designed by adopting a modified version of the OMACS framework. Second, such a design is transformed into a P-graph model to take advantage of the combinatorial nature of the underlying structure. Third, algorithm SSG of the P-graph framework is used to generate all feasible assignment sets, which represents the different ways agents can play roles to achieve goals in the organization. Fourth, for each assignment set, a Markov chain is constructed, which captures the behavior of the system; finally, algorithm \({R}_{O}\) is executed on each Markov chain to compute their steady states (either success or failure) for further analysis. The proposed approach is validated through the simulation of two organization-based multiagent systems from the robotics domain.