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

We present ECHO (Error attribution through Contextual Hierarchy and Objective consensus analysis), a novel algorithm for error attribution in LLM multi-agent systems. While existing approaches struggle with accuracy and reliability in complex interaction scenarios, ECHO combines hierarchical context representation, objective analysis-based evaluation, and consensus voting to improve attribution accuracy. Our approach leverages positional-based contextual understanding with objective evaluation criteria. Experimental results demonstrate that ECHO outperforms existing methods across various multi-agent scenarios, particularly for subtle reasoning errors and complex interdependencies. This structured framework provides a more robust solution for error attribution in collaborative AI systems.