Distributed cooperative applications (e.g.,e-commerce) 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. As relative importance of agents may evolve during the course of computation and problem solving,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. One important issue is then: what kind of information could be used to estimate which agents are most critical agents? In this paper, we will first introduce our prototype architecture for adaptive replication. Then, we will discuss various kinds of information and strategies to estimate criticality of agents: static dependences, dynamic dependences, roles, norms, and plans. Some preliminary measurements and future directions will also be presented.