Structured Multi-Agent Pipeline Outperforms Existing Methods for Root Cause Analysis on Real-World Telemetry Data

Jul 16, 2026

A new preprint demonstrates that both classical causal discovery and existing LLM-based multi-agent systems struggle to reliably identify root causes in production microservice failures using the OpenRCA dataset. The authors introduce a Structured Multi-Agent RCA pipeline that significantly outperforms these baselines and propose a reverse reasoning agent to diagnose failures as stemming from either reasoning gaps or data ambiguity. Their analysis shows that the main bottleneck is not data access but the agent's reasoning ability, highlighting the need for improved model-level reasoning.

Why it matters: This work clarifies that advances in model reasoning, rather than data pipeline improvements, are crucial for effective root cause analysis in complex telemetry data.

Full story at: arXiv AI/ML ↗