Growing Harness is a training paradigm that learns agent control structures from task feedback, converting recurring decisions into reusable executable code rather than keeping them in LLM context. The approach reduces LLM calls by 76-92% and inference costs by 74-99% while maintaining or improving task success rates across multiple benchmarks and model sizes.