VibeLayer Development: An Architectural Pattern for Long-Running Autonomous Software Development

Abstract Large Language Model (LLM)-based software engineering excels at short tasks but fails on multi-day workflows due to memory loss, inconsistent reasoning, and lack of self-correction mechanisms. We propose VibeLayer Development (VLD), an architectural pattern that integrates existing technologies—persistent external storage, ordered event logging, LLM-based planning, and hybrid validation—into a unified six-layer framework. While each component is individually established, we argue that their specific integration achieves emergent properties unattainable by any subset: deterministic state reconstruction, long-term consistency, and closed-loop self-improvement. We present a formal model of VLD's state transitions, analyze the emergent properties arising from layer integration, and propose an evaluation framework with four metrics for assessing long-horizon agent stability. We hypothesize that VLD will significantly reduce task duplication and regression bugs compared to existing systems, and outline planned experiments to validate these claims.