Phase 7: Production Observability & Incident Response · 45 min · Langfuse · LangSmith · Arize Phoenix

AI-Native Observability

An LLM failure doesn't throw a 500 — it returns 200 OK with a fluent, wrong answer. Standard infrastructure monitoring will never see it.

Real-world application: Being able to reconstruct a specific bad production response from trace data alone — without re-running the request — is the concrete skill that separates teams who can debug AI incidents from teams that can only speculate.

What you will learn

Name what a production trace must capture: input, resolved prompt, model/version, token counts, latency, tool calls, output
Explain the four production health signals: quality score, p95 latency, error/refusal rate, cost per request
Explain why LLM failures are invisible to standard infrastructure monitoring (uptime, HTTP status, latency alone)
Reconstruct why a specific production response went wrong using only its stored trace record

Introduction

AI-Native Observability

Objective

Capture full trace records for simulated agent requests and reconstruct why a specific bad response occurred using only its stored trace.

Unlock the full lesson

You've read the first 2 sections. The rest of this lesson covers The Problem, The Concept, Build, Check Yourself, Key Terms & Next — plus a hands-on lab, quiz, and project artifact.

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