Key takeaways
- Define incident classes, severity, owners, kill switches, communication, and recovery before deployment.
- Contain credentials, queues, scheduled work, outbound channels, spend, and data access before debating the model response.
- Preserve a redacted timeline of inputs, sources, versions, tool calls, approvals, state changes, and affected records.
- Correct customer and business state, test the fix against the failure, and restore authority gradually.
01
Name the incidents the workflow can create
Examples include an unsupported public claim, wrong recipient, consent violation, budget change, deleted or overwritten record, exposed customer data, poisoned retrieval source, repeated tool action, biased decision, or silent failure that leaves inquiries unanswered. Classify severity by affected people, data sensitivity, financial or legal impact, scale, reversibility, and ongoing exposure.
Assign incident lead, technical owner, business owner, privacy or security contact, communications owner, and vendor contact. Publish the path for users and employees to report a problem.
02
Build containment controls around side effects
Prepare switches to disable the workflow, revoke or rotate credentials, pause queues and schedules, stop outbound sends, freeze budget changes, block affected tools, and fall back to a manual route. Test the switches; a runbook that depends on the engineer who is on leave is not ready.
NIST’s AI RMF calls for post-deployment monitoring, incident response, recovery, change management, appeal, override, and communication. Agent incidents are operational events across the full system, not bad text alone.
Swipe to compare every column
| Phase | Immediate question | Evidence |
|---|---|---|
| Contain | Which side effects are still possible? | Credentials, queues, schedules, channels |
| Assess | Who and what may be affected? | Records, runs, versions, time range |
| Correct | What state and communication need repair? | Before/after and owner |
| Restore | What test justifies limited return? | Regression eval and monitored scope |
03
Preserve the timeline without spreading the data
Record discovery time, reporter, model and workflow version, source provenance, relevant inputs and outputs, tool calls, policy checks, approvals, state changes, affected objects, vendor status, containment actions, and decisions. Restrict sensitive evidence and avoid pasting it into broad incident channels.
Do not wait for perfect root cause before containing a credible high-impact path. At the same time, avoid declaring the model solely responsible when stale CRM data, an overly broad token, weak validation, or a human-approved action enabled the outcome.
04
Restore service in smaller steps than it left
Correct customer records, campaign state, budgets, permissions, and external communication. Notify affected people when appropriate through the organization’s approved process. Add the incident to the evaluation set and test the control that should have stopped it.
Return in observe or recommendation mode, monitor a narrow population, then expand only when evidence supports it. Finish with an owner and due date for every systemic action; “remind the team to be careful” is not a control.
Primary sources and further reading
Use the source material to validate details against your own context and current platform configuration.
- NIST AI RMF Core
- NIST Generative AI Profile
- NIST AI Risk Management Framework
- OWASP GenAI: Excessive Agency
This field note follows the XenGrowth editorial policy: primary sources where available, visible limitations, material review dates, and no invented first-hand experience.
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