Tags or observable features shared by a group of similar agents are effectively used in real and artificial societies to signal intentions and can be used to infer unobservable properties and choose appropriate behaviors. Use of tags to select partners has been shown to produce stable cooperation in agent populations playing the Prisoner's Dilemma game. Existing tag mechanisms, however, can promote cooperation only if that requires identical actions from all group members. We propose a more general tag-based interaction scheme that facilitates and supports significantly richer coordination between agents. Our work is motivated by previous research that showed the ineffectiveness of current tag schemes for solving games requiring divergent actions. The mechanisms proposed here not only solves those problems but are effective for other general-sum games. We argue that these general-purpose tag mechanisms allow new application possibilities of multiagent learning algorithms as they allow an agent to reuse its learned knowledge about one agent when interacting with other agents sharing the same observable features.