Many current AI systems address failure by repeatedly retrying the same or slightlymodified processes. While this approach can occasionally produce successful outcomes, itdoes not guarantee convergence, nor does it systematically reuse information obtained fromfailure.This paper proposes a conceptual framework in which post-failure correction is treatednot as a sequence of operations, but as an abstract corrective intent. A corrective intentrepresents the semantic purpose of a modification required to resolve a failure, independentof specific implementations or execution mechanisms.Within this framework, failures, corrective intents, and re-executions are considered asstructurally related elements of a single process, rather than isolated retry attempts. Thisperspective distinguishes retry-based exploration from intent-guided correction, where repeated executions are conceptually interpreted as adjustments toward predefined successconditions.The contribution of this paper is not an algorithm, implementation, or empirical evaluation. Instead, it provides a clear problem formulation, introduces the notion of correctiveintent, and outlines a high-level structural view of self-correcting information processing systems. The framework is intended to serve as a conceptual foundation for future discussionson reliability, convergence, and design principles of autonomous and generative systems.