You can move repetitive agent control logic from expensive LLM context into persistent, reusable code—cutting inference costs by 74-99% while keeping smaller models effective on complex tasks.
This paper introduces Growing Harness, a method that automatically builds reusable agent control code from task feedback instead of asking language models to repeatedly solve the same control problems. By learning executable code that handles recurring decisions, the approach reduces LLM calls by 76-92% while maintaining or improving task success rates across different model sizes.