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（LLM）的智能体正从被动文本生成器演变为具备规划、工具使用、检索、记忆访问、环境交互及多智能体协作能力的自主系统。这些能力增强了智能体的自主性，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、哪些证据支持各项主张、工具调用是否合理、记忆如何影响后续决策，或故障源于何处。本综述将证据追踪与执行溯源作为可信赖大语言模型智能体中过程级问责制的基础进行考察。我们定义执行溯源为智能体执行过程的类型化图，而证据追踪则为其在证据-支持关系上的投影。该视角将检索关联性、主张支持度、工具使用安全性、记忆谱系、可观测性、调试、审计与恢复统一纳入一个框架。我们提出一套分类体系，涵盖溯源来源、证据与执行单元、溯源关系、追踪粒度与时序、表征形式及信任功能。继而回顾关键方法论方向，包括溯源表征、证据归因、工具使用溯源、运行时防护栏、附带溯源的记忆、可观测性及故障诊断。最后，我们围绕构建可溯源感知、可审计、可恢复的智能体系统，探讨了基准测试、数据集、评估指标及开放性挑战。