This paper examines whether human-in-the-loop (HITL) checkpoints—common in AI agent deployments—actually improve or degrade performance. The authors hypothesize that frequent human intervention imposes a measurable "intervention penalty" on AI agents, similar to how micromanagement affects human workers. They formalize this penalty mathematically and explore it through simulation, finding that intervention frequency may be a stronger predictor of task degradation than residual capability variation. The paper argues for "structured self-governance" over action-level oversight in software development contexts where AI capability is sufficient.
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Julian Ramirez
Sofia Plajutin
Bose (United States)
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Ramirez et al. (Thu,) studied this question.
www.synapsesocial.com/papers/69d893c96c1944d70ce04ccf — DOI: https://doi.org/10.5281/zenodo.19457264