New framework uses LLMs and graph databases for autonomous system diagnostics

Researchers have introduced Semantic Observability, a novel multi-agent framework designed to streamline diagnostics in complex distributed microservice systems. This system unifies vast amounts of heterogeneous log data into a dynamic Property Graph Database, modeling infrastructure, software dependencies, and event sequences. By employing Directed Cyclic Graphs (DCGs) and causal graph algorithms, the framework autonomously identifies the scope of anomalies and utilizes a large language model (LLM) to generate precise, code-level root cause analyses and actionable remediation steps. The proposed architecture aims to reduce mean time to resolution, decrease LLM token consumption, and prevent hallucinations in diagnostic outputs. AI

IMPACT Could significantly reduce diagnostic time and costs in complex IT systems by automating root cause analysis.

RANK_REASON The item is an academic paper detailing a new system architecture and methodology for AI-driven diagnostics. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

• alphaXiv
• CatalyzeX
• DagsHub
• Directed Cyclic Graphs
• Gotit.pub
• Hugging Face
• large language model
• Loghub
• Property Graph Database
• ScienceCast
• Semantic Observability
• train ticket

• paper
• infra

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →