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Software agents, artificial intelligent "representatives" of the interests of supply chain participants, enhance the effectiveness and efficiency of business to business (B2B) e-commerce systems that support modern supply chains. The inter-organizational transaction flow starts with a matchmaking process in which suppliers' agents are matched with demanders' agents. The matched agents then negotiate either directly or with the aid of an arbiter. The deal agreed to by the negotiating agents is then executed. The matchmaking and arbitration processes support the achievement of transactions that benefits both sides over many repeated transactions. Matchmaking pairs agents with the least conflicting interests, and arbitration seeks consensus between the negotiating parties within reasonable time and costs while discouraging insincere behavior by the participants. Both processes require a trusted, honest intermediary to achieve appropriate matchmaking and arbiter consensus among all the parties. In this paper we analyze matchmaking in a stable two sided market, where each seller is matched with a single buyer, and all agents on both sides are matched. We discuss matchmaking in conjunction with arbitration protocols, and analyze their performance through simulation.