Cursor harness aligns tools with model training

Optimizing the developer agent harness for model-specific training is crucial for reducing token costs and error rates. Incompatibility between the model's expected edit format and the provided tools directly increases reasoning token overhead.

• –OpenAI models are natively trained on patch-based file editing, whereas Anthropic models are trained to perform string replacements.
• –Forcing a model to use an unfamiliar editing format results in unnecessary reasoning token consumption and higher error rates.
• –Model-agnostic agent frameworks must implement deep customization, tailoring both prompts and tool interfaces to match each model's native training distribution.
• –These optimizations highlight why the execution harness is as critical to agent performance as the underlying LLMs themselves.