We study an agency model in which the principal has outcome data under different incentive schemes and aims to design an optimal contract under minimal assumptions about the way the agent responds to incentives. Events unfold as follows: (1) the principal offers a contract---a mapping from output to nonnegative payments; (2) the agent chooses costly action---a probability distribution over output; and (3) output and payoffs are realized. The principal has outcome data under K different contracts which, sidestepping estimation error, enables her to recover the action corresponding to each of these contracts. We assume that the agent best-responds to the offered contract and has quasi-linear preferences over money and actions, but we make no further assumptions about the production environment. The principal does not have prior beliefs about any of the unknown aspects of the environment. Instead, she seeks a contract that maximizes worst-case profit.