Recursive AI-augmented software engineering introduces a new class of development systems in which autonomous agents operate within iterative feedback loops, generating, evaluating, and refining software artifacts at machine speed. Without explicit governance boundaries, these systems are prone to recursive optimization drift, complexity collapse, and misalignment with human-defined objectives. This paper introduces reflector, a framework for reflective development systems that imposes structured governance contracts on recursive AI-assisted workflows. reflector defines four core mechanisms: (1) scoped autonomous agents with bounded execution contexts, (2) human-in-the-loop alignment checkpoints triggered by milestone events, (3) recursive auditing systems that continuously validate system state against defined invariants, and (4) milestone synchronization protocols that gate autonomous progression. We describe the theoretical foundations of reflective development, characterize common failure modes arising from unbounded recursion, and present an architectural reference model for implementing reflector-compliant systems.
Alan Szmyt (2026) studied this question.