Learning agents can autonomously improve both knowledge and performances by using learning strategies. Recently, an approach based on a cloning process, called EVolutionary Agents (EVA), has been proposed to obtain more effective recommendations, generating advantages for the whole agent community through individual improvements. In particular, users can substitute unsatisfactory agents with others provided with a good reputation and associated with users having similar interests. This approach is able to support an evolutionary behavior in the community that allows the best agents to emerge over the less productive agents. However, such an approach is user-centric requiring a user's request to clone an agent. Consequently, the approach slowly generates modifications in the agent population. To speed up this evolutionary process, a proactive mechanism called EVA2 is proposed in this paper, where the system autonomously identifies for each user those agents that in the community have a good reputation and share the same interests. The user can check the clones of such suggested agents in order to evaluate their performances and to adopt them. The results of some experiments show significant advantages introduced by the proposed approach.