AIBullisharXiv – CS AI · Jun 237/10
🧠Holmes is a multi-agent AI system that automates root cause analysis for mobile app crashes in large-scale production environments by synthesizing runtime signals like stack traces and logs without requiring local reproduction. Deployed at WeChat, it achieves 87.6% accuracy in fault localization and reduces debugging time from hours to 77 seconds, demonstrating practical AI applications in enterprise software reliability.
AIBearisharXiv – CS AI · Mar 56/10
🧠Research reveals that AI agents used for cloud system root cause analysis fail systematically due to architectural flaws rather than individual model limitations. A study analyzing 1,675 agent runs across five LLM models identified 12 failure types, with hallucinated data interpretation and incomplete exploration being the most common issues that persist regardless of model capability.
AINeutralarXiv – CS AI · Jun 236/10
🧠Researchers introduce PAG-RCA, a framework for root cause analysis in complex systems that accounts for unobserved latent variables using Partial Ancestral Graphs. The methodology combines causal identification with partial identification bounds to diagnose system failures reliably even when data is scarce or incomplete, outperforming existing approaches on synthetic and real-world infrastructure benchmarks.
AINeutralarXiv – CS AI · Jun 236/10
🧠Researchers present OpsCortex, a multi-agent system that uses persistent operational memory and dependency graphs to automatically derive root causes of microservice failures, then leverages LLMs only for explanation rather than diagnosis. The architecture separates root-cause derivation from explanation, addressing a critical gap in autonomous operations by maintaining structured system knowledge that typical monitoring stacks discard.
AINeutralarXiv – CS AI · Jun 196/10
🧠Researchers introduce JustDiag, an AI-powered diagnostic justification engine that improves root cause analysis (RCA) by maintaining explicit process states, competing hypotheses, and evidence tracking rather than relying solely on fluent final answers. Evaluated on 66 real-world incidents, the system demonstrates stronger accountability and process quality in high-stakes operational environments where transparency and calibrated uncertainty matter more than confident completion.
AINeutralarXiv – CS AI · Jun 106/10
🧠A research thesis addresses critical limitations in automated anomaly detection and root cause analysis (RCA) for microservice systems by introducing integrated methods that leverage multiple data types and establishing standardized benchmarking frameworks. The work combines anomaly detection with RCA, incorporates event data alongside traditional metrics, and eliminates dependency on service call graphs while advancing causal inference techniques.
AINeutralarXiv – CS AI · Jun 96/10
🧠Researchers present Causal Agent Replay (CAR), a new method for diagnosing why large language model agents fail by identifying which decision step caused a failure rather than just which action executed it. Using structural causal models and intervention-based analysis, CAR achieves significantly higher attribution accuracy than existing LLM-judge approaches and provides confidence-bounded explanations for agent failures.
AINeutralarXiv – CS AI · Jun 96/10
🧠Researchers present Graph Traversal Agent, an LLM-based root cause analysis system for Kubernetes incidents that combines graph-guided reasoning with deterministic validation tools. The system demonstrates significant performance improvements on benchmarks but acknowledges limitations in production environments and benchmark-specific coupling.
AINeutralarXiv – CS AI · Jun 16/10
🧠Researchers formalize causal pathway analysis for rare events in structural equation models, proposing testable implications that depend on causal abstractions rather than complete system graphs. This work bridges verbal explanations and rigorous causal modeling, enabling root cause analysis of outliers with reduced computational complexity.
AINeutralarXiv – CS AI · May 276/10
🧠Researchers have introduced ORCA, an AI copilot system designed to make causal analysis accessible to domain experts across manufacturing, medicine, and social science. The tool automates root cause analysis workflows while allowing users to control the level of automation, from fully automatic to highly guided execution, addressing a significant accessibility gap in complex analytical methods.
🏢 Microsoft
AINeutralarXiv – CS AI · May 126/10
🧠Krone-viz is an interactive visualization system that uses hierarchical log abstraction and LLM augmentation to detect, localize, and explain anomalies in system logs. The tool transforms unstructured flat log sequences into semantically coherent units, enabling more effective anomaly diagnosis for software engineers and system operators.
AINeutralarXiv – CS AI · Mar 27/1018
🧠Researchers have developed LumiMAS, a comprehensive framework for monitoring and detecting failures in multi-agent systems that incorporate large language models. The framework features three layers: monitoring and logging, anomaly detection, and anomaly explanation with root cause analysis, addressing the unique challenges of observing entire multi-agent systems rather than individual agents.
