A Survey for LLM Agent Trajectory Analysis: from Failure Attribution to Enhancement

Large language model (LLM) agents have emerged as a transformative software paradigm, enabling autonomous task execution through natural language reasoning, multi-agent collaboration, and tool integration. However, their non-deterministic decision-making and complex interaction patterns introduce unprecedented challenges for failure diagnosis and system improvement. Unlike traditional software, where failures often manifest as code defects, agent failures are embedded in lengthy, language-heavy execution trajectories that include natural language reasoning, obscuring root causes and complicating repair. This survey provides a systematic review of the burgeoning field of trajectory analysis for failure attribution and system enhancement in LLM agents. We collect 55 papers published between early 2025 and April 2026, spanning software engineering, artificial intelligence, and human-computer interaction. We organize the literature along five key dimensions: failure taxonomy, failure attribution, system enhancement and optimization, trajectory monitoring and analysis tools, as well as datasets and benchmarks. Our analysis reveals that while the field has progressed rapidly from simple LLM prompting to causal inference, fine-tuned tracer models, and dynamic intervention, the step-level attribution accuracy remains limited, and benchmark diversity is still a bottleneck. We identify four complementary taxonomic perspectives, four methodological paradigms for attribution, and three families of enhancement strategies. We also critically assess existing benchmarks, highlighting a recent shift toward full observability and unrecoverable failure annotation. This survey serves as both a structured overview for current research and a strategic guide for advancing robust, efficient, and self-improving agent ecosystems. We further call upon the software engineering community to engage more deeply with this emerging class of software, bringing expertise in fault localization, program repair, and empirical methods to bear on the unique challenges posed by LLM agents.