Agent performance depends heavily on how you orchestrate their behavior—by making this orchestration code readable and portable through natural language, you can reuse and improve agent designs much more easily.
This paper proposes a new way to design agent control systems by writing them in natural language instead of buried in code. The authors create Natural-Language Agent Harnesses (NLAHs) and a runtime system that executes these harnesses, making it easier to reuse, compare, and study how agents are controlled across different tasks.