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observability-llm-obs

Answer questions about LLM and agentic-application behavior from data already ingested into Elastic: latency and error rate, token and cost utilization, response quality and guardrail events, and agentic call-chain orchestration. Use when the user asks about LLM monitoring, GenAI observability, token spend or AI cost, model latency, prompt or guardrail failures, or how an agent's tool-call chain executed.

skills/observability/llm-obs/SKILL.md

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Nov 6, 2026
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# LLM and Agentic Observability

Answer questions about monitoring LLMs and agentic components using **data actually ingested into Elastic** — nothing
else. The four questions this skill answers are LLM performance, cost and token utilization, response quality, and call
chaining or agentic workflow orchestration.

A given deployment typically uses **one or more** ingestion paths: APM/OTLP traces, and/or integration metrics and logs.
Which one exists is a discovery result, not an assumption — never assume both are present. For ES|QL syntax, commands,
and query patterns, use the **elasticsearch-esql** skill. For service-level latency and error triage that is not
LLM-specific, use the **observability-sre-triage** skill.

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## Environment Configuration