A graph-guided LLM agent framework for microservice root cause analysis reports AC@1 accuracy of about 74% on two benchmarks, outperforming the strongest LLM baseline by roughly 26 percentage points.
Title resolution pending
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.SE 1years
2026 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
GALA: Graph-Augmented LLM Agents for Root Cause Analysis and Incident Response in Microservices
A graph-guided LLM agent framework for microservice root cause analysis reports AC@1 accuracy of about 74% on two benchmarks, outperforming the strongest LLM baseline by roughly 26 percentage points.