Agentic RCA for Internet-Scale Services Using Constrained Creativity

{'Problem': 'Existing troubleshooting systems for Internet-scale services exhibit significant capability gaps, particularly in automating complex problem-solving tasks. This paper addresses these limitations by introducing a new approach that leverages large language models (LLMs) to enhance troubleshooting efficiency and effectiveness. The work is presented as a preprint and has not undergone peer review.', 'Method': 'The proposed system, named E4, employs a paradigm of constrained creativity, which integrates LLM-assisted automation with a structured domain-specific language (DSL). The DSL is specifically designed to facilitate troubleshooting by being restricted to loop-free data flow programs and incorporating high-level operators. This structured approach allows for more effective problem-solving while maintaining the flexibility needed for diverse troubleshooting scenarios.', 'Results': 'The implementation of E4 demonstrates a 62% improvement in accuracy compared to state-of-the-art troubleshooting solutions. Additionally, the system achieves a remarkable 12x reduction in operational costs when compared to existing systems, showcasing its potential for significant resource efficiency in Internet-scale service management.', 'Limitations': 'The authors do not report any limitations in their work, indicating confidence in the robustness and applicability of their proposed system. However, the absence of reported limitations may warrant further scrutiny in practical deployments.', 'Why it matters': 'The implications of this research are substantial for the field of automated troubleshooting in large-scale systems. By improving accuracy and reducing costs, E4 could enable more efficient management of Internet-scale services, potentially leading to enhanced service reliability and user satisfaction. This work lays the groundwork for future research into LLM applications in operational contexts, encouraging further exploration of constrained creativity paradigms in AI-driven automation.'}