Randomized trial demonstrates an autonomous root cause analysis system in distributed systems, suggesting improved observability and reliability.
Key Points
The aim is to develop an autonomous root cause analysis (RCA) system that leverages both deterministic algorithms and large language models (LLMs) for effective signal processing and hypothesis validation.
The system utilizes a BFS-driven architecture for efficient signal fetching and correlation.
An agentic loop is employed for hypothesis validation with a maximum of 5 iterations to avoid runaway inference.
Evidence-led reasoning is implemented to ensure conclusions are based on verifiable causal relationships.
The RCA system effectively generates and validates hypotheses while maintaining high reliability through deterministic scoring.
It scales investigation depth based on incident size rather than graph complexity, enhancing practical usability.
The architecture enables seamless integration with observability tools like Prometheus and Jaeger.