Observability for Intelligent Systems

Observability is a property of a system, not a feature added to it. A system is observable if you can understand its internal state from its external outputs. For infrastructure platforms, this means being able to diagnose any problem without modifying the system or deploying additional tooling.

Why observability requires architectural commitment

Observability cannot be bolted on after the fact. A system designed without observability in mind produces metrics that don't align with failure modes, logs that omit critical context, and traces that don't capture the right causal relationships. In Fabric, observability is a first-class design constraint. Every component is designed to emit structured telemetry. The telemetry schema is defined before the component is implemented, not after.

The three pillars

• Metrics answer: what is the system doing right now? GPU utilization, memory pressure, network throughput, and queue depths are examples of infrastructure metrics that matter for AI workloads
• Traces answer: what happened to this specific request or job? A trace of a training job submission through the scheduler reveals where latency occurs and where failures happen
• Logs answer: what was the system doing when this specific event occurred? Structured logs with consistent schemas make it possible to correlate events across components

AI-specific observability

AI infrastructure has observability requirements beyond what general-purpose monitoring covers. GPU utilization is a poor proxy for training efficiency — a GPU can be 100% utilized and making no useful progress if it is waiting for data. Storage I/O rates, data pipeline throughput, and batch size must be visible alongside GPU metrics. Training convergence signals — loss curves, gradient norms, learning rate schedules — belong in the same observability stack as CPU and network metrics.