[2603.00623] TraceSIR: A Multi-Agent Framework for Structured Analysis and Reporting of Agentic Execution Traces

Computer Science > Artificial Intelligence arXiv:2603.00623 (cs) [Submitted on 28 Feb 2026] Title:TraceSIR: A Multi-Agent Framework for Structured Analysis and Reporting of Agentic Execution Traces Authors:Shu-Xun Yang, Cunxiang Wang, Haoke Zhang, Wenbo Yu, Lindong Wu, Jiayi Gui, Dayong Yang, Yukuo Cen, Zhuoer Feng, Bosi Wen, Yidong Wang, Lucen Zhong, Jiamin Ren, Linfeng Zhang, Jie Tang View a PDF of the paper titled TraceSIR: A Multi-Agent Framework for Structured Analysis and Reporting of Agentic Execution Traces, by Shu-Xun Yang and 14 other authors View PDF HTML (experimental) Abstract:Agentic systems augment large language models with external tools and iterative decision making, enabling complex tasks such as deep research, function calling, and coding. However, their long and intricate execution traces make failure diagnosis and root cause analysis extremely challenging. Manual inspection does not scale, while directly applying LLMs to raw traces is hindered by input length limits and unreliable reasoning. Focusing solely on final task outcomes further discards critical behavioral information required for accurate issue localization. To address these issues, we propose TraceSIR, a multi-agent framework for structured analysis and reporting of agentic execution traces. TraceSIR coordinates three specialized agents: (1) StructureAgent, which introduces a novel abstraction format, TraceFormat, to compress execution traces while preserving essential behavioral informati...