Reliability Before Autonomy: Building an AI Reliability Control Plane for Multi-Cloud LLM and Agentic AI Systems

Production AI systems can appear healthy at the infrastructure layer while model quality degrades, agents loop, tool failures cascade, latency fluctuates, and token costs surge. Across multiple clouds and model providers, probabilistic outputs, tool-use errors, cascading agent failures, variable latency, GPU scarcity, provider outages, cross-cloud dependencies, and escalating token costs can degrade an AI workflow even when every infrastructure component appears healthy. This session will focus on building an AI Reliability Control Plane for operating production LLM and Agentic AI systems in multi-cloud environments. The control plane presents a unified operational layer for service-level objectives, end-to-end observability, model and agent health monitoring, intelligent workload routing, automated failover, policy enforcement, security guardrails, cost governance, and human oversight. Attendees will learn to define reliability indicators and SLOs for models, agents, and complete workflows; detect and contain failures before they propagate; and implement policy-driven routing, fallback, and recovery across clouds and model providers.