Distributed Tracing & Observability

Distributed Tracing & LLM Observability (OpenTelemetry, LangSmith, Arize Phoenix, OpenLLMetry) provides the comprehensive telemetry and telemetry telemetry recording full lifecycles across production LLM and Agent pipelines; injecting a globally unique `Trace ID` and nested `Span IDs` at ingress, the framework instruments every execution stage: 1) Latency Telemetry: Time To First Token (TTFT), Time Per Output Token (TPOT), and total inference duration; 2) Cost & Token Auditing: tracking prompt tokens, completion tokens, prefix-cached tokens, and monetary cost attribution per tenant/model; 3) Runtime Payload Capture: recording fully rendered prompt templates, retrieved RAG chunk scores, Agent tool execution inputs/outputs, and safety guardrail verdicts.

Production LLM troubleshooting, debugging multi-step Agent reasoning loops/deadlocks, tenant cost chargebacks, and RAG hallucination auditing.

Classical APMs only trace raw HTTP spans, leaving LLM inference internals and multi-agent loops as opaque black boxes; LLM observability renders the full execution graph transparent from network ingress to individual token generation.

Define the calculation formulas and hardware bottlenecks of TTFT (Time To First Token) vs TPOT (Time Per Output Token)?

Design OpenTelemetry-compliant semantic conventions for LLM tracing across prompt tokens, completion tokens, and tool invocations?

How does nested Span tree topology record Agent Thought-Action-Observation loops and trigger watchdog circuit breaks on infinite tool loops?

Design an inline PII masking interceptor scrubbing credit cards, passwords, and PII from prompts before persisting spans to ClickHouse/Jaeger?

How to integrate asynchronous RAGAS span evaluators computing live Faithfulness and Answer Relevance metrics without blocking user responses?