a frozen robot policy performs far better inside a good agent harness, but harnesses are hand-built per robot. Here a coding model watches rollouts, diagnoses failures, and iteratively rewrites the harness (which also evolves its own persistent memory, e.g. learned grasp depths); success rises from 9.7% to 43.3% on unseen scenes and 12.7% to 40.2% on unseen tasks, and the evolved harness often beats the finetuned policy. Robotic setting, but the pattern — iterate the harness, keep the model frozen — is the fleet's own playbook.