Where Did It All Go Wrong? A Hierarchical Look into Multi-Agent Error Attribution

• URL: http://arxiv.org/abs/2510.04886v2
• Date: Thu, 16 Oct 2025 18:25:19 GMT
• Title: Where Did It All Go Wrong? A Hierarchical Look into Multi-Agent Error Attribution
• Authors: Adi Banerjee, Anirudh Nair, Tarik Borogovac,
• Abstract summary: We present ECHO, a novel algorithm that combines hierarchical context representation, objective analysis-based evaluation, and consensus voting to improve error attribution accuracy.<n> Experimental results demonstrate that ECHO outperforms existing methods across various multi-agent interaction scenarios.
• Score: 0.7226144684379191
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Error attribution in Large Language Model (LLM) multi-agent systems presents a significant challenge in debugging and improving collaborative AI systems. Current approaches to pinpointing agent and step level failures in interaction traces - whether using all-at-once evaluation, step-by-step analysis, or binary search - fall short when analyzing complex patterns, struggling with both accuracy and consistency. We present ECHO (Error attribution through Contextual Hierarchy and Objective consensus analysis), a novel algorithm that combines hierarchical context representation, objective analysis-based evaluation, and consensus voting to improve error attribution accuracy. Our approach leverages a positional-based leveling of contextual understanding while maintaining objective evaluation criteria, ultimately reaching conclusions through a consensus mechanism. Experimental results demonstrate that ECHO outperforms existing methods across various multi-agent interaction scenarios, showing particular strength in cases involving subtle reasoning errors and complex interdependencies. Our findings suggest that leveraging these concepts of structured, hierarchical context representation combined with consensus-based objective decision-making, provides a more robust framework for error attribution in multi-agent systems.

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