Distributed cooperative applications are now increasingly being designed as a set of autonomous entities, named agents, which interact and coordinate (thus named a multi-agent system). Such applications are often very dynamic: new agents can join or leave, they can change roles, strategies, etc. This high dynamicity creates new challenges to the traditional approaches of fault-tolerance. In this paper, we will focus on crash failures, with usual preventive approaches by replication. But, as criticality of agents may evolve during the course of computation and problem solving, static design is not appropriate. Thus we need to dynamically and automatically identify the most critical agents and to adapt their replication strategies (e.g., active or passive, number of replicas), in order to maximize their reliability and their availability. In this paper, we describe a prototype architecture, supporting adaptive replication. We also discuss and compare various control strategies for replication, one using agent roles, and another using inter-agent dependences as types of information to infer and estimate criticality of agents. Experiments and measurements are also reported.