AI 中文总结
针对智能体网络的传统可靠性措施无法覆盖的失效模式，提出Reliability Assurance Intelligence架构，通过服务可靠性配置文件和上下文胶囊保障服务可靠性与问责性，以确定性网络服务为例验证该架构。
AI 中文摘要
智能体网络通过自主推理、自适应规划、工具使用及跨域协调，将已接受的意图转化为可运行服务，但这些能力引入了传统可靠性措施无法完全覆盖的失效模式。已接受的意图仍可能被错误执行，例如系统基于过时信息行动、重复外部操作、仅应用部分变更，或进入悄悄放宽策略执行的回退模式。此类失效可能使服务看似正常运行，实则行为不安全或不可问责，且缺乏足够证据检测、解释或从失效中恢复。本文提出通用保障架构Reliability Assurance Intelligence（RAI，可靠性保障智能），从服务描述中推导每项服务的可靠性配置文件，明确需检查、记录、恢复和审计的内容；运行时，通用函数利用服务配置文件，在适配服务条件的上下文胶囊中保留恢复和问责所需的持久状态。以智能体生命周期管理器用于确定性网络服务为运行示例，本文设计RAI架构并提出验证其可靠性保障的方法。
英文摘要
Agentic networks transform accepted intents into operational services through autonomous reasoning, adaptive planning, tool use, and cross-domain coordination, but these capabilities introduce failure modes that conventional reliability measures do not fully capture. An accepted intent may still be carried out incorrectly, for example because the system acts on stale information, repeats an external action, applies only part of a change, or enters a fallback mode that quietly relaxes policy enforcement. Such failures can leave a service running and apparently healthy while its behavior is unsafe or unaccountable, with too little evidence to detect, explain, or recover from them. This article proposes Reliability Assurance Intelligence (RAI), a general assurance architecture for such systems. From the service description, RAI derives a per-service reliability profile that states what must be checked, recorded, recovered, and audited. At runtime, generic functions use the service profile to retain the durable state needed for recovery and accountability in a context capsule adapted to service conditions. Using an agentic lifecycle manager for deterministic network services as a running example, we design the RAI architecture and propose a methodology for validating its reliability assurances.
Comments7 pages, 4 figures, 2 tables, 15 references
