As organizations rapidly integrate AI into production systems, a new class of failures is emerging — unpredictable, non-deterministic, and often invisible to traditional observability.

This talk explores real-world challenges faced when AI-driven systems intersect with production reliability, including:

Unbounded decision loops causing cascading failures
Silent data corruption from model drift
Latency spikes due to inference unpredictability
Security risks introduced by autonomous actions

Drawing from hands-on experience operating large-scale distributed systems, this session introduces a new reliability model for AI systems, where traditional SLOs and monitoring are no longer sufficient.

We will present a practical framework for:

Designing AI-aware SLOs (beyond latency & availability)
Implementing guardrails for autonomous systems
Building observability for non-deterministic behavior
Integrating Zero Trust principles into AI pipelines

Attendees will leave with actionable strategies to safely deploy AI in production without compromising reliability, security, or user trust.