Lesson 1283 of 2116
AI Observability Stack 2026: Traces, Metrics, and Cost in One Pane
Building a unified view across LangSmith, Datadog LLM Observability, OpenTelemetry, and custom dashboards.
Lesson map
What this lesson covers
Learning path
The main moves in order
- 1The premise
- 2Comparing agent-specific observability tools (Arize, Helicone, Langfuse)
- 3The premise
Concept cluster
Terms to connect while reading
Section 1
The premise
AI observability is logs + traces + cost + quality — one of the four is always missing in vendor pitches.
What AI does well here
- Capture full prompt/response with PII scrubbing at ingest
- Tag every call with user, route, prompt version, and model
- Correlate cost and latency to user-visible outcomes
- Alert on quality regressions, not just error rates
What AI cannot do
- Replace a real eval suite for quality monitoring
- Surface novel failure modes without sample-based human review
- Hide the bill for storing every prompt forever — define retention
Key terms in this lesson
Section 2
Comparing agent-specific observability tools (Arize, Helicone, Langfuse)
Section 3
The premise
Generic APM does not understand tool calls, retries, and prompt versions — agent-aware tools do.
What AI does well here
- Capture full conversation traces with tool I/O
- Diff prompts and outputs across versions
What AI cannot do
- Replace metrics you cared about before LLMs (latency, error rate)
- Tell you why a model regressed semantically
Key terms in this lesson
End-of-lesson quiz
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