An autonomous evolutionary framework for construction of decision trees that requires no or minimal human interaction is presented. The framework evolves two types of agents which hold the discovered knowledge, and uses a non-standard implicit fitness evaluation in a co-evolving environment. Together with self-adaptation of evolutionary parameters and with some other improvements it can monitor and adjust its own behavior. This framework is a base for a specific implementation of a program for induction of decision trees. The program's capability to self-adapt to a given problem is used as a measure to predict if some dataset is difficult or even impossible to analyze. On average it produces very general solutions or gives no solution if the dataset is prone to the overfitting problem.