Phase 8: Production Hardening & Operations · 55 min · Python · LangSmith · PagerDuty

Incident Response & Runbooks

When it breaks at 2 AM, the runbook is your guide.

What you will learn

Run the incident response process: triage, investigate, mitigate, resolve, post-mortem
Debug in unfamiliar codebases: reading traces, reproducing production issues locally, log analysis
Handle AI-specific incidents: model degradation, retrieval failures, agent loops, guardrail bypasses, cost overruns
Write runbooks: trigger conditions, diagnostic steps, mitigation procedures, escalation contacts
Execute client handoff: training on-call teams, shadowing during transition, documentation for self-sufficiency

Objective

What you'll learn

This lesson takes approximately 55 min. By the end, you will be able to:

Run the incident response process: triage, investigate, mitigate, resolve, post-mortem
Debug in unfamiliar codebases: reading traces, reproducing production issues locally, log analysis
Handle AI-specific incidents: model degradation, retrieval failures, agent loops, guardrail bypasses, cost overruns
Write runbooks: trigger conditions, diagnostic steps, mitigation procedures, escalation contacts
Execute client handoff: training on-call teams, shadowing during transition, documentation for self-sufficiency

The Problem

When your AI system breaks at 2 AM, the runbook is your guide. Incident response for AI systems requires specific procedures: diagnosing a model provider outage vs. a retrieval failure vs. a data pipeline issue, communicating with the client during an incident, and writing the post-mortem that prevents it from happening again.

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Key terms in this lesson

Agent