Ensembles of classifiers are often used to achieve accuracy greater than any single classifier. The predictions of these classifiers are typically combined together by uniform or weighted voting. In this paper, we approach the ensembles construction under a multi-agent framework. Each individual agent is capable of learning from data, and the agents can either be homogenous (same learning algorithm) or heterogeneous (different learning algorithm). These learning agents are combined by a meta-agent that utilizes evolutionary algorithm, using the accuracy as fitness score, for discovering the weights for each individual agent. The weights are indicative the best searched combination (or collaboration) of the set of agents. Experimental results show that this approach is a valid model for ensemble building when compared to the best individual agent and a simple plurality vote of the agents.