AI Agent Incident Response: What to Do When Agents Go Wrong

AI agents fail in ways that are qualitatively different from traditional software failures. A server crash is obvious. An agent that starts confidently giving subtly wrong answers can run for hours before anyone notices — and by then, hundreds of customers have received bad information. The AI incident response playbook: Step 1 is detection — your monitoring stack should alert on accuracy degradation, not just uptime. Set alerts for deflection rate drops above 10%, CSAT falls below threshold, and error rate spikes. Step 2 is containment — the first action in any AI incident is routing traffic away from the affected agent to fallback (human agents or a known-good previous version). Do not debug in production. Step 3 is diagnosis — pull the conversation logs from the incident window, identify the failure pattern, trace to root cause (model regression, data change, prompt issue, tool failure). Step 4 is remediation — fix the root cause in staging, validate against the incident cases, deploy. Step 5 is postmortem — document what failed, why it was not caught earlier, and what monitoring improvements prevent recurrence. Blameless postmortems produce better outcomes than blame-focused ones.