OpenObserve’s SRE Agent uses LLMs to automatically correlate alerts, logs, metrics, and traces, giving teams a unified view of incidents. It surfaces dependencies, patterns, and RCA insights in minutes reducing investigation time and improving response quality.
Understand how LLMs automate incident correlation across logs, metrics, traces, and alerts to eliminate manual investigation work and improve RCA quality.
See a live demonstration of the SRE Agent mapping service dependencies, grouping related alerts, surfacing historical patterns, and generating actionable RCA reports with contributing factors and prevention steps.
Learn how to reduce resolution latency in your own environment by leveraging automated incident analysis and historical learning to cut investigation time from hours to minutes.
When incidents strike distributed systems, missing correlation between alerts, logs, metrics, and traces wastes hours of investigation time and leads to poor RCA quality at the L1/L2 support level. The business consequences are real: extended resolution latency, misrouted tickets, and on-call burnout.
OpenObserve's SRE Agent leverages LLMs to automate incident correlation: analyzing past incidents to surface relevant factors , mapping service dependencies, grouping related alerts algorithmically, and surfacing historical patterns. You'll see a live demonstration of the SRE Agent correlating multi-signal telemetry, visualizing alert graphs during incidents, and producing RCA reports that cut investigation time from hours to minutes.
Shraddha Kishan is a backend developer with experience in Rust, Linux, and AI. With extensive experience at building products and features zero-to-one, he excels at driving business value through unique solutions and automation.
Manas is a passionate Dev and Cloud Advocate with a strong focus on cloud-native technologies, including observability, cloud, kubernetes, and opensource communities--building bridges between tech and community.
