The intricate control logic that dictates agent performance, often termed 'harness engineering,' is typically embedded deep within controller code and tied to specific runtime conventions. This opacity makes it exceptionally difficult to transfer, compare, or scientifically analyze agent harnesses. The researchers propose a paradigm shift, introducing Natural-Language Agent Harnesses (NLAHs) as a solution.
From Code to Conversation: A New Harness Paradigm
NLAHs reframe harness behavior as editable natural language artifacts. This approach decouples high-level control logic from the underlying implementation details. By expressing harness logic in a human-readable format, the researchers aim to make agent control more accessible, understandable, and scientifically rigorous. This externalization is key to treating harness design as a first-class scientific object, rather than a buried implementation detail.
