What question did this study set out to answer?

The research aims to enhance automated root cause analysis (RCA) in microservices by integrating structured diagnostic tools with language models.

July 26, 2026Open Access

Tools, Not Code: Microservice Root Cause Analysis with Cost-Effective Language Models

View Full Paper

Ask AI

Bookmark

Authors

YKYoungdong Kim KSKihoon Shin HKHanjae Kim

Discussion

Member takes

Overview

Randomized trial demonstrates a tool-augmented approach improves root cause analysis accuracy in microservices, suggesting optimal architectural strategies.

Key Points

The research aims to enhance automated root cause analysis (RCA) in microservices by integrating structured diagnostic tools with language models.
Developed a tool-augmented multi-agent RCA system.
Implemented a four-stage pipeline including deterministic triage, LLM-based hypothesis formation, targeted hypothesis testing, and ensemble prediction.
Tested the system on the OpenRCA benchmark with 335 failure scenarios across four microservice systems.
Improved binary accuracy from 7.5% to 12.8% and partial score from 15.9% to 37.9% compared to code-generation paradigm.
Deterministic triage contributed the largest accuracy gain by compiling domain knowledge into preprocessing.
Multi-agent investigation enhanced fault-type discrimination but resulted in occasional over-falsification.

Cite This Study

Kim et al. (2026) studied this question.

synapsesocial.com/papers/6a65a660d3aea3239cd77cc4 https://doi.org/10.5281/zenodo.21503088

View Full Paper

Ask AI

Bookmark

July 26, 2026Open Access

Tools, Not Code: Microservice Root Cause Analysis with Cost-Effective Language Models

View Full Paper

Ask AI

Bookmark

Authors

YKYoungdong Kim KSKihoon Shin HKHanjae Kim

Discussion

Member takes

Overview

Randomized trial demonstrates a tool-augmented approach improves root cause analysis accuracy in microservices, suggesting optimal architectural strategies.

Key Points

The research aims to enhance automated root cause analysis (RCA) in microservices by integrating structured diagnostic tools with language models.
Developed a tool-augmented multi-agent RCA system.
Implemented a four-stage pipeline including deterministic triage, LLM-based hypothesis formation, targeted hypothesis testing, and ensemble prediction.
Tested the system on the OpenRCA benchmark with 335 failure scenarios across four microservice systems.
Improved binary accuracy from 7.5% to 12.8% and partial score from 15.9% to 37.9% compared to code-generation paradigm.
Deterministic triage contributed the largest accuracy gain by compiling domain knowledge into preprocessing.
Multi-agent investigation enhanced fault-type discrimination but resulted in occasional over-falsification.

Cite This Study

Kim et al. (2026) studied this question.

synapsesocial.com/papers/6a65a660d3aea3239cd77cc4 https://doi.org/10.5281/zenodo.21503088

Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

View Full Paper

Ask AI

Bookmark