Multi-agent systems are used in domains where individual component autonomy and cooperation are necessary. The overall system performance requires that the diverse agents maintain quality interactions to facilitate cooperation. A complication to inter-agent interaction occurs when the agents learn (change their own functionality), when new agents are introduced, or existing agents are functionally modified. This research focuses on creating a general use multi-agent system, Middleware Unifying Framework for Independent Nodes System (MUFFINS), and implementing a mechanism, the Megagent, that addresses the interaction challenges. The Megagent provides the ability for agents to assess their performance per data source and to improve it with transformations based on feedback. Evaluation of the concept is tested on data mangled from the Digits dataset to represent learning and new agents and in all cases improves accuracy over a static agent.