LLM copilots for SRE: Accelerating incident response with production-safe guardrails

Modern platform teams face escalating challenges as distributed systems grow complex. Moving from alert detection to root cause identification has become a primary bottleneck in incident response. While large language models offer powerful pattern-matching capabilities, deploying them without constraints risks hallucinations and privilege escalation. This session presents a production-ready architecture for LLM-powered incident copilots that accelerate the Sense-Decide-Act cycle while maintaining human oversight. The copilot ingests observability data from OpenTelemetry traces, metrics, and logs to generate situational summaries and root-cause hypotheses. The speaker will detail guardrail design including PII redaction, privilege boundaries, and verification gates. Platform engineers gain practical blueprints for integrating AI copilots while maintaining reliability guarantees.