指定委托自治边界：智能体人工智能的需求工程
Specifying the Delegated-Autonomy Boundary: Requirements Engineering for Agentic AI
- Faculty of IT, Monash University（莫纳什大学信息科技学院）
- University of Duisburg-Essen（杜伊斯堡-埃森大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
研究智能体人工智能系统带来的需求工程问题，提出机构理由记录和智能体委托策略两个互补工件，通过分级建模权限，以医院出院协调智能体和自动代码审查智能体为例阐释框架，解决委托自治边界相关需求工程问题。
AI中文摘要：
智能体人工智能系统不仅进行预测或推荐，还能在外部环境中自主规划、维护状态并行动，这以一种特定且未充分探讨的方式改变了需求工程问题，引入了委托自治边界。当前实践将相关决策隐藏在提示、工具模式和运行时策略中，而它们实则是需求层面的承诺。本文提出两个互补工件，一是机构理由记录（AJR）助团队决定何时选用智能体而非更简单选项；二是智能体委托策略（ADP）涵盖安全有效开发所需指定的内容，且ADP中的权限是分级建模的。文中用医院出院协调智能体和自动代码审查智能体两个对比示例阐释了该框架。
英文摘要：
Agentic AI systems do not just predict or recommend; they plan, maintain state, and act in external environments with varying degrees of autonomy. This changes the requirements engineering problem in a specific and under-addressed way: it introduces what we call the delegated-autonomy boundary -- the set of decisions about what may be delegated to the system, under what graduated authority, with what oversight, and how control is returned. Current practices bury these decisions inside prompts, tool schemas, and runtime policies, even though they are requirements-level commitments. This paper proposes two complementary artifacts. First, an Agency Justification Record (AJR) helps teams decide when an agent is warranted over simpler alternatives. Second, an Agentic Delegation Policy (ADP) captures what must be specified for safe and effective development: purpose, authority, information, coordination, assurance, and evolution. Crucially, authority in the ADP is modelled as graduated, i.e., a tiered structure. We illustrate the framework with two contrasting examples: a safety-critical hospital discharge coordination agent and an automated code review agent.
