From Local Errors to System Failures: Modeling and Benchmarking Error Propagation in Multi-Agent LLM Systems

Errors in LLM-based multi-agent systems (MAS) can propagate through intermediate messages and compromise final answers, yet outcome-based evaluation obscures how agents amplify, attenuate, or correct them. We propose Response-Aware Error Propagation among Agents (REPA), a structural model that connects measured local responses to system-level error propagation. REPA separately estimates input assembly and agent processing across multiple error channels, while theoretical analysis relates local modeling errors to endpoint prediction error. Experiments span three Qwen base models, six MAS workflows, and ten knowledge and reasoning settings in ErrPropBench-10. REPA achieves a mean Spearman correlation of 0.84 across base models, compared with 0.47 for direct regression. Controlled experiments also demonstrate its utility for targeted recovery. REPA-guided RAG improves accuracy over no retrieval by an average of 19 percentage points with 16% additional tokens. By linking local agent behavior to system susceptibility, REPA provides an interpretable foundation for studying error propagation and developing targeted optimization in MAS.