一句话：提出 AJR 与 ADP 两类需求制品，明确智能体 AI 的委托自主边界、分级授权与监督回收。
智能体 AI 不仅预测或推荐，还会规划、维护状态并在外部环境中以不同自主度行动，由此引入一个尚未充分处理的需求工程问题——委托自主边界：哪些决策可委托给系统、授予何种分级权限、如何监督以及如何收回控制。现有实践将这些需求级承诺埋藏在提示、工具模式和运行时策略中。本文提出两个互补制品：Agency Justification Record (AJR) 帮助判断何时智能体优于更简单方案；Agentic Delegation Policy (ADP) 规约目的、权限、信息、协调、保证与演化，并将权限建模为分层结构。论文用安全关键型医院出院协调智能体和自动代码审查智能体两个对比案例说明框架。
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.