AI INCIDENT RESPONSE
After the incident.
Your AI did something it shouldn't have: a wrong answer that cost money, an agent that acted out of bounds, a transcript you never wanted to read. You need three things fast: to know exactly what happened, to know what else is exposed, and to be able to show your customers, your board, or your regulator that it cannot quietly happen again. That is the work we do.
THE DIAGNOSIS
An incident is a symptom. Its class is the disease.
The instinct after an incident is to patch the one prompt, the one response, the one workflow that failed. We reproduce the failure, then red-team the whole class it belongs to: every input pattern, escalation path, and edge case that reaches the same weakness. You find out what else was exposed while it still costs nothing to know. You will not be alone: documented AI incidents rose 55 percent in 2025, while the share of organizations rating their own incident response excellent fell.
THE FIX
The incident becomes an eval.
We trace the failure to its root cause: the data, the prompt, the model, or the process around it, and fix it where it lives. Then the failure gets pinned: it becomes a permanent case in your eval suite, run against every future change, so this class of failure can never quietly return. Guardrails catch the pattern at runtime. Monitoring watches for drift back toward it.
What you take to the people you answer to is evidence: what happened, why, what changed, and the standard the system now passes continuously.
AFTERWARD
Come out stronger.
Incident response is the reliability loop entered at its most urgent point, and everything it builds stays: the evals, the guardrails, the observability. Most companies fund this layer after something breaks. The ones that come out stronger use the incident to build the standard they should have had, and keep running the loop from there.
COMMON QUESTIONS
Asked after incidents, answered plainly.
What should we do in the first 48 hours?
Preserve everything: transcripts, logs, prompts, model versions. Contain the exposed surface with guardrails or a scoped rollback. And resist the urge to silently patch the one failing prompt: the incident is a symptom of a class, and fixing the case while missing the class is how it happens again in front of a regulator.
Do we have to take the system down?
Rarely the whole system. The right move is usually containment: guardrail the failing pattern, narrow the blast radius, and keep the value running while the root cause gets fixed and pinned as a permanent eval.
What do we tell the board, our customers, or a regulator?
Evidence, in their language: what happened, why, what changed, and the standard the system now passes continuously. Courts have already held companies liable for AI output, from the Air Canada chatbot ruling to a US federal court treating a chatbot as a product for liability purposes. A documented standard is the strongest position available.
Can this actually be prevented from recurring?
The class can. Every failure becomes a permanent eval run against every future change, guardrails catch the pattern at runtime, and monitoring watches for drift back toward it. Documented AI incidents rose 55 percent in 2025; the companies that come out stronger are the ones that convert incidents into standards.
LET'S TALK
Bring us the hard problem.
We'll bring the team that ships.