AI losses that arise through an insured organization's generative or agentic AI system require state reconstruction, not merely event reconstruction, because the relevant state changes as the system reasons, retrieves, calls tools, and acts. The relevant question is not only what loss occurred, but what the system was allowed to do, what it actually did, and whether that reconstructed loss can support insurance claim recovery. This paper addresses losses in which the insured's AI system is in the causal chain, including externally triggered failures such as prompt injection, retrieval-augmented generation (RAG) poisoning, malicious tool output, credential misuse, and data poisoning. Specifically, this paper introduces CER, a use-case-level diagnostic for AI residual risk transfer. C (control boundary) asks whether the system had an enforceable operating envelope. E (evidence reconstruction) asks whether the system state and causal chain can be reconstructed from retained artifacts. R (insurance response) asks whether the reconstructed loss is insured: whether insurance coverage is available in the market and placed for the insured, together with the proof needed to support insurance claim recovery. The paper makes three contributions: it defines the AI-specific reconstruction problem, operationalizes that problem through CER, and specifies claim-grade evidence for AI reconstruction. Public examples include the reported PocketOS and Replit agentic database-deletion incidents and Moffatt v. Air Canada as an adjudicated output/reliance case. Keywords: AI systems; CER framework; residual risk transfer; agentic AI; generative AI; AI insurance; evidence reconstruction.

翻译：由被保险组织的生成式或代理式人工智能系统引发的人工智能损失需要进行状态重构，而非仅事件重构，因为相关状态会随系统推理、检索、调用工具和行动而发生变化。关键问题不仅在于发生了什么损失，还在于系统被允许做什么、它实际做了什么，以及这种重构的损失能否支持保险索赔追偿。本文探讨被保险的人工智能系统处于因果链中的损失，包括外部触发的故障，例如提示注入、检索增强生成（RAG）投毒、恶意工具输出、凭据滥用和数据投毒。具体而言，本文引入了CER，一种用于人工智能残余风险转移的用例级诊断方法。C（控制边界）询问系统是否具有可执行的操作范围。E（证据重构）询问系统状态和因果链是否可以从保留的痕迹中重构。R（保险响应）询问重构的损失是否投保：即市场上是否有保险覆盖并已为被保险人投保，以及支持保险索赔追偿所需的证据。本文做出三项贡献：定义了人工智能特定的重构问题，通过CER将该问题操作化，并指定了人工智能重构的索赔级证据。公开案例包括报道的PocketOS和Replit代理式数据库删除事件，以及作为已裁决的输出/依赖案例的Moffatt诉Air Canada案。关键词：人工智能系统；CER框架；残余风险转移；代理式AI；生成式AI；人工智能保险；证据重构。