observability-llm-obs

name	observability-llm-obs
description	Monitor LLMs and agentic apps: performance, token/cost, response quality, and workflow orchestration. Use when the user asks about LLM monitoring, GenAI observability, or AI cost/quality.
metadata	{"author":"elastic","version":"0.1.0","source":"elastic/agent-skills//skills/observability/llm-obs"}

LLM and Agentic Observability

Monitor LLMs and agentic components using data ingested into Elastic. Focus on performance, cost/token utilization, response quality, and call chaining.

Where to look

• Trace data (APM / OTel): traces* for LLM spans from OTel/EDOT instrumentations
• Integration metrics/logs: metrics* and logs* from Elastic LLM integrations (OpenAI, Azure, Bedrock, Vertex AI)
• Discover first: Use GET _data_stream or GET traces*/_mapping to find available data

Data available

From traces (traces*)

Purpose	Example attribute names (OTel GenAI)
Operation / provider	gen_ai.operation.name, gen_ai.provider.name
Model	gen_ai.request.model, gen_ai.response.model
Token usage	gen_ai.usage.input_tokens, gen_ai.usage.output_tokens
Errors	error.type

Use duration and event.outcome for latency and success/failure. Use trace.id and parent/child relationships for call chaining analysis.

Use cases and query patterns

LLM performance

FROM traces*
| WHERE @timestamp >= "2025-03-01T00:00:00Z" AND @timestamp <= "2025-03-01T23:59:59Z"
  AND span.attributes.gen_ai.provider.name IS NOT NULL
| STATS request_count = COUNT(*), failures = COUNT(*) WHERE event.outcome == "failure",
    avg_duration_us = AVG(span.duration.us)
  BY span.attributes.gen_ai.request.model
| EVAL error_rate = failures / request_count
| LIMIT 100

Token usage over time

FROM traces*
| WHERE @timestamp >= "2025-03-01T00:00:00Z" AND @timestamp <= "2025-03-01T23:59:59Z"
  AND span.attributes.gen_ai.provider.name IS NOT NULL
| STATS input_tokens = SUM(span.attributes.gen_ai.usage.input_tokens),
    output_tokens = SUM(span.attributes.gen_ai.usage.output_tokens)
  BY BUCKET(@timestamp, 1 hour), span.attributes.gen_ai.request.model
| SORT @timestamp
| LIMIT 500

Agentic workflow (trace-level view)

FROM traces*
| WHERE @timestamp >= "2025-03-01T00:00:00Z" AND @timestamp <= "2025-03-01T23:59:59Z"
  AND span.attributes.gen_ai.operation.name IS NOT NULL
| STATS span_count = COUNT(*), total_duration_us = SUM(span.duration.us) BY trace.id
| WHERE span_count > 1
| SORT total_duration_us DESC
| LIMIT 50

Workflow

- [ ] Step 1: Determine available data (traces*, metrics*, integration data streams)
- [ ] Step 2: Discover LLM-related field names (mapping or sample doc)
- [ ] Step 3: Run ES|QL queries for the user's question
- [ ] Step 4: Check active alerts/SLOs on LLM-related data
- [ ] Step 5: Summarize findings from ingested data only

Guidelines

• Use only data collected in Elastic. Do not rely on external UIs.
• Discover field names from _mapping or sample documents before querying.
• Prefer ES|QL and Elasticsearch APIs over Kibana UI.
• Use LIMIT and coarse time buckets for performance.