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.
Intelligent Harness Runtime: Orchestrating Language-Driven Agents
To execute these novel harnesses, the paper introduces the Intelligent Harness Runtime (IHR). IHR acts as a shared execution environment that leverages explicit contracts and durable artifacts to run NLAHs. The runtime includes lightweight adapters designed to bridge the gap between the natural language specifications and the actual execution environment. Controlled evaluations on coding and computer-use benchmarks demonstrate the operational viability of this approach, including module ablation studies and successful code-to-text harness migration.