Cloud-native systems have outgrown the incident response playbook. Static runbooks, manual operator intervention, and reactive on-call rotations weren't designed for the scale and complexity of modern distributed systems — and the result is longer outages, inconsistent recoveries, and burned-out engineers.

This session introduces an agentic AI approach to disaster recovery, where LLM-based agents don't just advise — they act. Multiple collaborating agents work in concert to detect anomalies, reason about root causes, evaluate recovery strategies, and execute remediation directly through your existing Kubernetes and cloud interfaces. Human oversight remains intact through safety controls for high-impact operations, so you keep the guardrails without the bottlenecks.

We'll show real-world results: dramatic reductions in MTTR and operational toil, with measurable improvements in recovery consistency and reliability. More importantly, we'll walk through the architecture — how agents are orchestrated, how they reason under uncertainty, and how this system evolves from a passive advisory tool into an autonomous SRE co-pilot.
If you're an SRE, platform engineer, or architect wondering where AI fits into your reliability story, this session gives you a concrete, battle-tested answer.