In order to make large-scale multi-agent systems reliable, we propose an adaptive application of replication strategies. Critical agents are replicated to avoid failures. As criticality of agents may evolve during the course of computation and problem solving, we need to dynamically and automatically adapt the number of replicas of agents, in order to maximize their reliability and availability based on available resources. We are studying an approach and mechanisms for evaluating the criticality of a given agent (based on application-level semantic information, e.g. messages intention, and also system-level statistical information, e.g., communication load) and for deciding what strategy to apply (e.g., active or passive replication) and how to parameterize it (e.g., number of replicas).