This paper focuses on improving load balancing algorithms in grid environments by means of multi-agent systems. The goal is endowing the environment with an efficient scheduling, taking into account not only the computational capabilities of resources but also the task requirements and resource configurations in a given moment. In fact, task delivery makes use of a Collaborative/Cooperative Awareness Management Model (CAM) which provides information of the environment. Next, a Simulated Annealing based method (SAGE) which optimizes the process assignment. Finally, a historic database which stores information about previous cooperation/collaborations in the environment aiming to learn from experience and infer to obtain more suitable future cooperation/collaboration. The integration of these three subjects allows agents define a system to cover all the aspects related with load-balancing problem in collaborations grid environment.
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