Delegation-scoped execution is not identifiable from standard observables: audit logs and execution traces can be identical under multiple incompatible delegation assignments. This gap is especially acute in LLM-based agentic systems, where agents dynamically select tools, vary execution sequences across runs for the same instruction, and spawn cooperating sub-agents. These dynamics fragment and interleave traces, making delegation-scoped reconstruction from causal structure alone structurally underdetermined. Although individual actions are authorized and logged, existing audit, tracing, and security schemas lack the semantics to reconstruct what actions occurred under a given delegation across heterogeneous systems. We focus on delegation-scoped attribution and access/share footprint reconstruction, not intent inference or reasoning reconstruction. We present an agent-aware observability substrate consisting of a lightweight gateway and a common information model that binds delegation context at execution time. This enables reliable cross-tool delegation-scoped reconstruction and direct forensic queries without heuristic time-window correlation.

翻译：委托作用域的执行无法从标准可观测数据中辨识：审计日志和执行轨迹在多个互不兼容的委托分配下可能完全一致。这一缺陷在大语言模型驱动的智能体系统中尤为突出——此类系统中的智能体动态选择工具、针对同一指令在不同运行过程中变更执行序列、并生成协作子智能体。这些动态特性导致执行轨迹碎片化与交错化，使得仅凭因果结构重构委托作用域在结构上具有欠定性。尽管各动作均经过授权并记录在案，但现有审计、追踪及安全模式缺乏足够语义，无法在异构系统中重构特定委托下发生的动作。本研究聚焦于委托作用域的归因追踪及访问/共享印记重构，不涉及意图推断或推理过程重建。我们提出一种智能体感知的可观测性基础架构：包含轻量级网关与通用信息模型，该模型能在执行时绑定委托上下文。此架构可实现跨工具委托作用域的可靠重构，并支持直接取证查询，无需依赖启发式时间窗口关联。