The Supply Chain Management track of the international Trading Agents Competition (TAC SCM) was introduced in 2003 as a test-bed for researchers interested in building autonomous agents that act in dynamic supply chains. TAC SCM provides a challenging scenario for existing AI decision-making algorithms, due to the high dimensionality and the non-determinism of the environment, as well as the combinatorial nature of the problem. In this paper we present RedAgent, the winner of the first TAC SCM competition. RedAgent is based on a multi-agent design, in which many simple, heuristic agents manage tasks such as fulfilling customer orders or procuring particular resources. The key idea is to use internal markets as the main decision mechanism, in order to determine what products to focus on and how to allocate the existing resources. The internal markets ensure the coordination of the individual agents, but at the same time provide price estimates for the goods that RedAgent has to sell and purchase, a key feature in this domain. We describe RedAgent?s architecture and analyze its behavior based on data from the competition.