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We have designed configurable agents to represent users in online auctions, specificMly the Michigan AuctionBot. The agents can be configured, started, and monitored from a web interface. We implemented three types of agents, distinguished by their different ways of using information in the auctions. A competitive agent does not use any information in the auction market. It chooses its actions based on its individual optimization problem. A price modeling agent uses price history as its only information. A bidder-modeling agent uses other agents’ bidding histories to predict their next bids and infer the next clearing price. Our experiments suggest that an agent’s performance in the auctions depends not only on its bidding strategy, but also on the bidding strategies of others. When all the agents behave strategically they may reach a sub-optimal equilibrium, in which they receive worse payoffs than behaving competitively.