Incident response when an AI model misbehaves needs the same structured discipline as any production incident: detect, triage by severity, contain, remediate and run a blameless postmortem, adapted for failure modes specific to AI like hallucination spikes, biased outputs or a cost spike from a runaway agent loop. Detection should come from automated monitoring, quality score drops, error rate spikes, or unusual cost patterns, rather than relying solely on user complaints, since many AI failures are subtle enough that an affected user may not realize the output was wrong. Containment options specific to AI incidents include an instant rollback through a prompt registry, disabling a tool or agent capability causing runaway behavior, or routing traffic away from a misbehaving self-hosted model to a backup provider through an LLM gateway. The runbook should define clear ownership for who can pull the rollback trigger without a lengthy approval chain, since failures involving hallucinated financial figures or inappropriate content can cause damage within minutes. A postmortem needs to identify not just the technical cause but whether existing evaluation and monitoring should have caught the issue before production, then feed that gap back into the golden evaluation dataset. Nanobase AI, an NVIDIA Inception Program member, builds these AI-specific incident runbooks and rollback mechanisms as part of production readiness for every deployment it manages.
Classify severity before reacting, using categories that fit AI failures
Generic incident severity scales, built around outages and latency, do not map cleanly onto AI-specific failures like a hallucination spike or a biased output pattern, which can be severe without ever showing up as an error rate or downtime metric.
| Severity | Example | Response time expectation |
|---|---|---|
| Sev 1 | Hallucinated financial or medical guidance reaching users; a runaway agent taking harmful automated actions | Immediate containment, minutes not hours |
| Sev 2 | Sustained quality or hallucination rate degradation affecting a large user segment | Contained within the current shift |
| Sev 3 | Isolated bad outputs, cost spike contained to one feature | Investigated within a business day |
| Sev 4 | Minor quality dip within normal variance, caught by monitoring but not yet user-impacting | Tracked, addressed in normal work |
Classifying by potential harm and scope, not just by whether an error was thrown, is what keeps a subtle but serious failure, a biased output pattern with no crash involved, from being triaged as a low-priority issue.
The containment toolkit needs to exist before the incident, not during it
- Instant prompt or configuration rollback through a versioned prompt registry, reverting to the last known-good version without a full redeploy.
- A capability kill switch that disables a specific tool or action an agent can take, stopping a runaway automated action without shutting down the entire system.
- Traffic rerouting away from a misbehaving self-hosted model to a backup provider or a previous model version through the LLM gateway, buying time to investigate without extending user exposure.
- A hard request-rate or budget cap to stop a cost spike from a looping agent while the root cause is investigated.
A team that has never tested these mechanisms discovers the friction, an undocumented rollback process, a missing permission to pull the trigger, during the incident itself, which is the worst possible time to learn it.
Who has the authority to act
The runbook needs to name, in advance, who can pull each containment lever without waiting for a lengthy approval chain, since a hallucinated figure reaching a customer or an agent taking an unintended action can cause damage within minutes, not the hours a full incident review process might take to convene. This does not mean skipping review, it means separating the authority to contain immediately from the more deliberate process of understanding root cause afterward.
Turning the postmortem into permanent coverage
A blameless postmortem for an AI incident should answer a question generic postmortems often skip: should existing evaluation and monitoring have caught this before it reached production. If the answer is yes, the specific failing case belongs in the golden evaluation dataset immediately, so the exact same failure mode is caught automatically before the next release rather than relying on the same vigilance that missed it the first time.
Frequently asked questions
How is an AI incident different from a typical software incident?
The failure is often subtle rather than a hard crash, a confidently wrong answer, a biased pattern across many interactions, a cost spike with no error thrown, so detection depends more heavily on quality and cost monitoring than on traditional error-rate alerting alone.
Should every AI incident trigger a full postmortem?
Sev 1 and Sev 2 incidents warrant a full blameless postmortem; lower-severity issues can be logged and reviewed in aggregate periodically, since a full postmortem process for every minor quality dip creates process fatigue without proportional benefit.
Who should own the AI incident runbook?
Ownership typically sits with whichever team owns production AI reliability day to day, often a platform or MLOps team, but the authority to pull specific containment levers should be distributed to whoever can act fastest for that specific lever, which is not always the same person who owns the overall runbook.
How Nanobase AI helps
Nanobase AI builds AI-specific incident runbooks, containment mechanisms and rollback infrastructure as part of production readiness for every deployment it manages, tested before launch rather than discovered during the first real incident.
Ready to discuss your project? Contact Nanobase AI or email hello@bumu.tech.