A wrong output is a systems event, not a prompt bug
When an AI workflow drafts the wrong refund amount, files a claim under the wrong policy, or routes a case to the wrong queue, the instinct is to open the transcript, tweak the prompt, and move on. That response treats the failure as a one-time wording problem instead of what it usually is: a gap somewhere in the system around the model.
The gap is rarely the model alone. It is more often stale or incomplete retrieval, a missing business rule, an ambiguous tool definition, or a permission that let a low-confidence action through unreviewed. A prompt edit can mask the symptom while leaving the actual gap in place, which is why the same class of failure tends to resurface a few weeks later in a slightly different shape.
Decide what counts as an incident before you need to know
Software teams have long used declared severity levels and clear triggers to decide when an issue becomes a tracked incident rather than a backlog item. Agentic workflows need the same threshold, set in advance rather than argued about in the moment.
A reasonable starting set: any write-back based on incorrect or fabricated evidence, any action that bypassed a required review gate, any customer-visible output that misstates a fact or policy, and any pattern of low-confidence actions that cleared automatically. Each of these deserves a name, an owner, and a response, not a private Slack thread that disappears once the immediate case is fixed.
- Wrong or fabricated evidence behind a completed action
- A review gate bypassed or a permission exceeded
- A customer-visible error in fact, amount, or policy
- A cluster of low-confidence actions that cleared automatically
Contain the action path, not just the conversation
The first response to a confirmed incident is containment, and containment means the workflow's ability to act, not just the specific reply that went wrong. If a workflow can write to a case management system, send an email, or submit a claim, the containment step is pausing or narrowing that permission until the cause is understood, the same way a bad software deploy gets rolled back before anyone debugs it live.
This is where versioned prompts, rules, and tool permissions pay for themselves. A workflow that can be reverted to its last known-good configuration in minutes turns an incident into a short interruption. A workflow where the current instructions and tool access live only in an ad hoc chat history turns the same incident into a longer outage, because nobody can say with confidence what the safe prior state was.
Review the system, not the transcript
Google's site reliability engineering practice popularized the blameless postmortem: a written review that assumes good intentions and asks what allowed the failure, not who caused it. The same structure works for an AI workflow incident, and it matters more here, because the instinct to blame "the model" is a way of avoiding the harder question of what the surrounding system allowed.
A useful review answers four questions in writing: what evidence or state the workflow was acting on, what rule or check should have caught the problem, why that check did not fire, and what changes to retrieval, rules, permissions, or review gates close the gap. The output is a short list of system changes, not a rewritten prompt and a promise to watch it more closely.
Track the failure classes OWASP already named
Teams do not need to invent a failure taxonomy from scratch. The OWASP Top 10 for LLM Applications catalogs recurring failure modes in deployed language model systems, including excessive agency, insecure handling of model output, and overreliance on unverified results. Mapping a real incident to one of these categories makes the review faster and makes the resulting fix easier to defend to the rest of the business.
Over time, a small log of incidents tagged by category shows where a workflow's actual risk concentrates, which is a better guide to where to invest next than a general sense that the system should be "more reliable."