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TraceSIR: A Multi-Agent Framework for Structured Analysis and Reporting of Agentic Execution Traces
arXiv – CS AI|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||1 views
🤖AI Summary
Researchers introduce TraceSIR, a multi-agent framework that analyzes execution traces from AI agentic systems to diagnose failures and optimize performance. The system uses three specialized agents to compress traces, identify issues, and generate comprehensive analysis reports, significantly outperforming existing approaches in evaluation tests.
Key Takeaways
- →TraceSIR addresses the challenge of analyzing complex execution traces from AI agentic systems that use external tools and iterative decision making.
- →The framework employs three specialized agents: StructureAgent for trace compression, InsightAgent for diagnosis, and ReportAgent for comprehensive reporting.
- →A new abstraction format called TraceFormat compresses execution traces while preserving essential behavioral information for analysis.
- →The researchers created TraceBench and ReportEval to evaluate the framework's performance across real-world agentic scenarios.
- →TraceSIR consistently produces coherent, informative, and actionable reports that outperform existing diagnostic approaches.
#ai-agents#machine-learning#trace-analysis#multi-agent-systems#llm#diagnostics#framework#research#automation#performance-optimization
Read Original →via arXiv – CS AI
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