Natural-Language Agent Harnesses

Understand

Agent performance is strongly shaped by the surrounding harness: the external execution system around a model that organizes a task run.

• Yet this logic is usually buried in tightly coupled controller code, which makes harnesses hard to inspect, compare, transfer, and ablate.
• This paper asks whether the reusable design pattern of an agent harness can be represented as an executable natural-language object.
• We introduce Natural-Language Agent Harnesses (NLAHs), editable documents that describe run-level harness policy, and Intelligent Harness Runtime (IHR), a shared runtime that interprets these documents into agent calls, handoffs, state updates, validation gates, and artifact contracts.