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This paper addresses the challenge of identifying root causes in production microservice failures using the OpenRCA dataset, which presents significant difficulties due to its large-scale, multimodal nature and lack of detailed domain knowledge. The authors demonstrate that both classical causal discovery methods and existing LLM-based approaches struggle with accuracy, while their Structured Multi-Agent RCA pipeline significantly outperforms these baselines by effectively leveraging both domain knowledge and knowledge-free modes. Key findings reveal that the primary limitations in root cause analysis stem from the reasoning capabilities of the models rather than data access, highlighting the need for improved model-level advancements to enhance reasoning performance.
Root cause analysis in microservices is hindered more by reasoning gaps than by data access, revealing a critical area for model improvement.
Identifying root causes in production microservice failures requires reasoning over large-scale, multimodal telemetry spanning metrics, logs, and traces, a problem that has proved resistant to both classical and LLM-based approaches. The OpenRCA dataset exemplifies these challenges: it is large-scale, multimodal, and lacks detailed domain knowledge, and yields consistently low accuracy across all existing methods. We show that classical causal discovery methods and existing LLM-based multi-agent systems fail to reliably identify root causes on this benchmark, and present a Structured Multi-Agent RCA pipeline that substantially outperforms existing LLM-based and classical baselines, supporting both domain-knowledge and knowledge-free operating modes. To diagnose where failures originate, we introduce a reverse reasoning agent that, given the correct answer, identifies which signals in the extracted anomalies support it and determines whether Stage~1 had access to those signals, classifying each failure as Reasoning Gap (evidence present but unused) or Data Ambiguity (evidence genuinely absent). This analysis reveals that the required evidence is present in the vast majority of failures: the bottleneck is not data access but the agent's ability to reason over it correctly. We further introduce an automated rule mining pipeline that systematically extracts discrimination rules from reverse reasoning reports, reducing reliance on manual knowledge curation. Across all configurations, model reasoning capability and domain knowledge are the primary constraints: stronger models embed more domain expertise, and explicit knowledge injection partially compensates for this gap. Reasoning performance remains practically bounded even when evidence extraction is perfect: scaffold engineering and better data pipelines alone cannot close this gap; progress requires improvements at the model level.
