Learning to Control LLM Agent Harnesses with Offline Reinforcement Learning

Large language model (LLM) agents are usually improved by changing prompts, models, or hand-written workflows, while the execution harness around the model is treated as fixed infrastructure. We argue that this harness is itself a learnable control layer. We formalize harness operation as a finite-horizon Harness MDP, where a lightweight controller selects structural execution actions while the LLM executor remains frozen. The controller is trained from offline rollouts using advantage-weighted

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ethics.ai (5 July 2026), “Learning to Control LLM Agent Harnesses with Offline Reinforcement Learning,” evidence record 222, https://ethics.ai/record/222 (originally published by arXiv).

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