RCLAgent uses multi-agent recursion-of-thought with parallel reasoning on trace graphs to outperform prior methods in root cause localization accuracy and efficiency for microservice systems.
Gala: Can graph-augmented large language model agentic workflows elevate root cause analysis?
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SREGym is an open-source benchmark of 90 live cloud failures for AI SRE agents, revealing up to 40-percentage-point differences in agent success across failure types.
citing papers explorer
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Towards In-Depth Root Cause Localization for Microservices with Multi-Agent Recursion-of-Thought
RCLAgent uses multi-agent recursion-of-thought with parallel reasoning on trace graphs to outperform prior methods in root cause localization accuracy and efficiency for microservice systems.
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SREGym: A Live Benchmark for AI SRE Agents with High-Fidelity Failure Scenarios
SREGym is an open-source benchmark of 90 live cloud failures for AI SRE agents, revealing up to 40-percentage-point differences in agent success across failure types.