Information about the powers and abilities of acting entities is used to coordinate their actions in societies, either physical or digital. Yet, the commonsensical meaning of an acting entity being deemed able to do something is still missing from the existing specification languages for the web or for multi-agent systems. We advance a general purpose abstract logical account of evidence-based ability. A basic model can be thought of as the ongoing trace of a multi-agent system. Every state records systemic confirmations and disconfirmations of whether an acting entity is able to bring about something. Qualitative inductive reasoning is then used in order to infer what acting entities are deemed able to bring about in the multi-agent system. A temporalised modal language is used to talk about deemed ability, actual agency, and confirmation and disconfirmation of deemed ability. What constitutes a confirmation and a disconfirmation is left to the modeller as in general it depends on the application at hand. So to illustrate the methodology we propose two extended examples, one in practical philosophy, the other in system engineering. We first use a logic of agency and ability to obtain a version of Mele's general practical abilities. Then, we look at the management of abilities in a supervised system.

The secret of success is honesty and fair dealing. If you can fake those, you've got it made.

This model of abstraction, call it the N&S model, is, what I will call a "context pro-functional" accumulator. It sets up a checkpoint for the selection of candidates in a given class of situation, call it [S1], to moving up the ladder of abstraction. It allows items to climb that meet certain functional requirements for an AI construction goal. It may even cull out an [S1]-dys-functional elements, if they are of concern. But such dysfunctional elements may be overlooked or disregarded, as being, for example of low probability of happening, or of low enough cost in the long run to allow to pass through.