AI Observability in Production: Tracing LLM Calls at Scale

Shipping an AI agent without observability is like deploying a microservice without logs. You'll have no idea why it fails, how much it costs, or where the latency hides. Here's the observability stack we recommend for production AI systems.

The Four Observability Pillars for AI

Standard observability (logs, metrics, traces) is necessary but not sufficient for AI systems. You also need:

• Prompt/response logging — Every LLM call with input tokens, output tokens, latency, model, and cost
• Distributed tracing — Correlating LLM calls across agent steps with a single trace ID
• Output quality metrics — Hallucination rates, confidence scores, human feedback signals
• Cost attribution — Token costs broken down by user, feature, and agent step

Tracing Architecture

Every agent invocation should produce a trace with nested spans. The structure we use:

Tooling Comparison

Critical Metrics to Track

• P50/P95/P99 latency per agent step — LLM calls are your primary latency driver; identify which model calls to cache or batch
• Token cost per request type — Break down costs by workflow type to identify optimization opportunities
• Retrieval precision — What fraction of retrieved chunks are actually used in the final response
• Error rate by agent node — Which steps fail most often; guards against silent failures in tool calls
• Human override rate — How often humans override agent decisions; high rates signal poor confidence calibration

Anomaly Detection

AI systems exhibit failure modes that traditional APM doesn't catch: sudden increases in token usage (prompt injection attempts), retrieval precision drops (index staleness), or response length spikes (hallucination indicators). Set up statistical process control alerts on these signals, not just infrastructure-level metrics.