做AI代理开发？这篇论文提出了AJR和ADP两个实用工具，帮你理清什么该委托给代理、怎么设定权限等级，比直接写prompt靠谱多了。
该论文提出代理型AI系统的需求工程需明确委托自治边界，即决定哪些任务可委托给系统、授权等级及监督方式。作者设计了两个工件：Agency Justification Record (AJR) 帮助团队判断何时需要代理而非简单替代方案；Agentic Delegation Policy (ADP) 涵盖目的、权限、信息、协调、保障和演化六个维度，其中权限采用分级模型。论文通过两个对比案例（安全关键的医院出院协调代理和自动代码审查代理）展示了框架应用。
Specifying the Delegated-Autonomy Boundary: Requirements Engineering for Agentic AI
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.