Recursive Self-Improvement at the Orchestration Layer: An Alternative Architecture for Autonomous Agent Evolution

The prevailing discourse on recursive self-improvement (RSI) assumes that meaningful self-improvement requires modification at the model level through changes to weights, architecture, or training methodology, that must be initiated by the model itself. This assumption drives both optimistic projections about artificial general intelligence and existential risk concerns about uncontrollable self-modification. This paper proposes an alternative: recursive self-improvement implemented at the orchestration layer, where autonomous agents monitor their own output quality, detect degradation patterns, and generate successor agents with clean operational contexts. This approach achieves the functional outcomes associated with RSI—progressive improvement in reasoning quality across iterative cycles—while preserving human oversight, audit capability, and governance integrity. The author presents a working architecture, the Recursive Persona Generator (RPG) framework, as a reference implementation demonstrating that orchestration-layer recursion is not theoretical but operational.