AI 中文总结
针对软件工程中基于LLM的多智能体系统难检查调试评估的问题，提出llmmas-otel工具，结合OpenTelemetry分布式追踪与故障注入，实现可复现的基线与故障执行对比，已在演示及真实系统上验证。
AI 中文摘要
基于大语言模型（LLM）的多智能体系统正被越来越多地探索用于软件工程任务，但在受控故障下仍难以检查、调试和评估。我们提出llmmas-otel，这是一种轻量级、与框架无关的工具，它将基于OpenTelemetry的分布式追踪与故障注入相结合，适用于软件工程工作流中基于LLM的多智能体系统。该工具在工作流阶段、智能体步骤、智能体间通信、工具调用以及LLM调用中，对智能体执行进行与追踪对齐的遥测检测，并支持在选定交互点进行针对性故障注入。这使得能够以可复现的方式比较基线执行与故障执行，并通过对齐的追踪和运行工件检查效果。我们描述了该工具的动机、架构、实现、当前能力，以及在最小演示工作流和一个用于软件开发的真实基于LLM的多智能体系统上的初始验证情况。
英文摘要
Large Language Model-based multi-agent systems are increasingly explored for software engineering tasks, but they remain difficult to inspect, debug, and evaluate under controlled failures. We present llmmas-otel, a lightweight and framework-agnostic tool that combines OpenTelemetry-based distributed tracing with fault injection for LLM-based multi-agent systems in software engineering workflows. The tool instruments agent executions with trace-aligned telemetry across workflow phases, agent steps, inter-agent communication, tool calls, and LLM invocations, and supports targeted fault injection at selected interaction points. This makes it possible to compare baseline and faulty executions in a reproducible way and inspect the effects through aligned traces and run artifacts. We describe the motivation, architecture, implementation, current capabilities, and initial validation of the tool on a minimal demo workflow and a real LLM-based multi-agent system for software development.
DOI：10.1109/ICST69053.2026.00037
