By representing logs as a connected graph of infrastructure and events rather than flat text, autonomous agents can efficiently isolate failure causes and explain them in code-level detail—cutting through noise that overwhelms traditional log analysis.
This paper presents Semantic Observability, a multi-agent system that automatically diagnoses failures in distributed systems by converting unstructured logs into a queryable graph database, then using causal reasoning and LLMs to pinpoint root causes and suggest fixes. It reduces manual work for engineers investigating system outages.