Large language model (LLM)-based agents are evolving from passive text generators into autonomous systems capable of planning, tool use, retrieval, memory access, environmental interaction, and multi-agent collaboration. These capabilities expand agent autonomy, but also make agent behavior harder to verify, debug, and audit. Final-answer accuracy alone cannot explain how an output was produced, which evidence supported each claim, whether tool calls were justified, how memory influenced later decisions, or where failures originated. This survey examines evidence tracing and execution provenance as foundations for process-level accountability in trustworthy LLM agents. We define execution provenance as the typed graph of an agent execution and evidence tracing as its projection onto evidence-support relations. This perspective connects retrieval grounding, claim support, tool-use safety, memory lineage, observability, debugging, audit, and recovery within a unified framework. We introduce a taxonomy covering trace sources, evidence and execution units, provenance relations, tracing granularity and timing, representation forms, and trust functions. We then review key methodological directions, including provenance representation, evidence attribution, tool-use provenance, runtime guardrails, provenance-bearing memory, observability, and failure diagnosis. Finally, we discuss benchmarks, datasets, metrics, and open challenges for building provenance-aware, auditable, and recoverable agent systems.

翻译：基于大语言模型的智能体正从被动文本生成器演变为具备规划、工具调用、检索、记忆访问、环境交互及多智能体协作能力的自主系统。这些能力增强了Agent的自主性，但也使其行为更难以验证、调试和审计。仅凭最终答案的准确性无法解释输出如何生成、每项主张由哪些证据支撑、工具调用是否合理、记忆如何影响后续决策，以及故障源于何处。本综述将痕迹追踪与执行溯因作为构建可信LLM Agent过程级问责机制的基础展开研究。我们将执行溯因定义为Agent执行过程的有向类型图，将痕迹追踪定义为其在证据-支持关系上的投影投影。这一视角将检索溯源、主张支持、工具使用安全、记忆谱系、可观测性、调试、审计与恢复统一整合至一个框架中。我们提出了一种分类体系，涵盖痕迹来源、证据与执行单元、溯因关系、追踪粒度与时机、表征形式以及信任功能。随后，我们系统评述了关键方法论方向，包括溯因表征、证据归属、工具使用溯源、运行时护栏、携带溯因的记忆、可观测性及故障诊断。最后，我们讨论了构建具备溯因感知、可审计且可恢复的Agent系统的基准测试、数据集、评估指标及开放挑战。