什么是 Agent Harness：概念定义与工作机制
Agent Harness: Definition
Agent harness 是围绕 AI 模型运行执行循环的配套软件，负责执行模型调用工具的请求、返回结果，并严格控制模型接收的信息与操作权限。大语言模型（LLM）本身无法独立执行动作，需借助 harness 校验权限并调用外部工具。该执行循环让 AI 模型从单纯回复提示词的聊天机器人，转变为能持续推进任务的 AI 智能体。
Summary: An agent harness manages a software loop between an AI model and its tools. Its context, permissions, and limits shape what users can do.
Imagine two AI scheduling assistants: one can copy-edit meeting agendas, while the other can check invitees’ calendars, find free time, and send meeting invitations. Both rely on the same AI model, but their capabilities are quite different. Their capabilities depend on the software that surrounds and supports the AI model, called the harness.
What Is an Agent Harness?
An agent harness (also called an agentic harness, or simply a harness) is the software around an AI model that runs its execution loop: it carries out the model’s requests to use tools, returns the results, and controls what information the model receives and what it is allowed to do.
At its core, a large language model (LLM) generates data by predicting what comes next in a sequence. It can generate structured requests to check a calendar or send an invitation, but it cannot perform these actions on its own. To act, it must ask other applications to do things for it by outputting commands. An agent harness handles each such request: it checks permissions, calls the appropriate tool, and returns the result to the AI model. The AI model can then request another action, or return an output to the end user. This cycle of request, action, and result is the execution loop. Running this loop is what transforms an AI model from a simple chatbot into an AI agent that can persistently work through a task, rather than just replying to a prompt.
来源：Nielsen Norman Group (NN/g) · nngroup.com