用于多智能体系统故障归因的自适应影响图

中文摘要 AI 辅助

多智能体大语言模型（LLM）系统正越来越多地部署在实际应用中，其故障可能造成高昂代价且难以定位。尽管自动故障归因的研究不断增加，但对失败运行的诊断仍在很大程度上依赖人类工程师。然而工程师很少通过逐行阅读原始日志来调试复杂系统，相反，可观测性工具会围绕组件、动作和依赖关系组织轨迹，以支持针对性导航。我们假设现代LLM能从同一范式中受益。为验证该假设，我们提出自适应影响图（AIGs），这是一个两阶段智能体框架，首先将失败轨迹转换为结构化图，随后对其进行导航以识别关键错误。在多个模型上的实验表明，更丰富的轨迹表示始终能提升故障归因效果，其中自适应图构建和智能体引导的遍历产生了最佳结果。AIGs在多智能体故障归因的标准基准Who&When上建立了新的最优性能，这证实了我们的假设：归因不仅取决于诊断模型，还取决于轨迹的表示与探索方式。

英文摘要

Multi-agent LLM systems are increasingly deployed in real-world applications, where failures can be costly and difficult to localize. Despite growing efforts to automate failure attribution, diagnosing failed runs still largely relies on human engineers. Yet engineers rarely debug complex systems by reading raw logs end to end. Instead, observability tools organize traces around components, actions, and dependencies to support targeted navigation. We hypothesize that modern LLMs can benefit from the same paradigm. To test this hypothesis, we introduce Adaptive Influence Graphs (AIGs), a two-stage agentic framework that first transforms a failed trace into a structured graph and then navigates it to identify the critical error. Across multiple models, we show that richer trace representations consistently improve failure attribution, with adaptive graph construction and agent-directed traversal yielding the strongest results. AIGs establish a new state of the art on Who&When, the standard benchmark for multi-agent failure attribution. This affirms our hypothesis that attribution depends not only on the diagnosing model, but also on how the trace is represented and explored.

发表机构

• Tel Aviv University（特拉维夫大学）
• AWS Agentic AI

机构由 AI 辅助整理，请以论文原文为准。