Detect and classify the event by impact and severity.
AI INCIDENT RESPONSE · CONTAINMENT · RECOVERY
AI incident response: contain, recover and learn without losing operational control.
AI incident response is the coordinated process for classifying a meaningful failure, limiting its impact, preserving evidence, moving the system into a safe state, restoring service and verifying that risk has returned to an acceptable level. For business agents this may mean disabling a tool, reducing autonomy, blocking writes, isolating a tenant, rolling back a version or requiring human approval. Observability detects and explains signals; incident response begins when those signals require containment or recovery. Governance defines who has authority to approve each measure.
Decision-oriented reading
14 sectionsStart with the process, review controls, then move to metrics, related routes and sources.
Connect this topicOperational guide →
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1. Define what the business considers an incident
Not every technical error is an incident. An automatic retry that resolves without impact may remain an operational event. By contrast, an out-of-policy action, an incorrect system change, information exposure, sustained degradation or a queue that prevents SLA compliance may require formal management.
Define categories and examples before a problem occurs. This avoids debate during a critical situation and gives operations, business and technology a shared language. Classification should consider impact, scope, reversibility, data sensitivity and the need for human intervention.
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2. Establish severity levels and owners
A minor issue may be handled within the operations team; an action involving financial impact or sensitive data may require leadership, security or compliance. Define severity levels with observable criteria and assign who coordinates, who investigates and who can authorise containment measures.
Severity also helps prioritise response times. Not every incident needs the same urgency, but every incident should have a known path. Clear roles reduce duplication and prevent several people from changing the system at the same time without coordination.
DOES IT FIT YOUR PROCESS?
Move from the idea to a diagnosis of your current operation.
Identify what to automate first, which data the workflow needs and where human control must remain.
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3. Design granular containment
When a problem occurs, shutting everything down is not always the best response. If one tool fails, you can block only that integration; if the issue affects writes, you can keep read access and drafts; if one organisation has inconsistent data, you can isolate that tenant without affecting others.
The ability to contain by tool, process, organisation or autonomy level reduces blast radius. Design these controls before an incident and test that they really work. An emergency option that has never been validated may fail precisely when it is needed most.
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4. Maintain an operational safe mode
A safe mode should allow the service to keep creating value with less risk. It can turn automatic actions into proposals, require human approval, disable write tools or limit operation to information retrieval. Controlled degradation is often better than a complete outage.
Define which functions remain available, what message users and customers receive and which conditions allow return to normal mode. Safe mode should not be improvised during a crisis; it should be part of the architecture and operating procedures.
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5. Prepare rollback for models, prompts, rules and connectors
Incidents can appear after changing a model version, an instruction, a policy or a connector. Every component capable of changing behaviour needs an identifiable version and a clear path back to the previous state.
Rollback should include compatible dependencies. Reverting a prompt without reverting a tool whose contract changed may not restore expected behaviour. Maintain a simple compatibility matrix and automate return paths where possible.
VALIDATE THE IMPACT
Before expanding automation, calculate whether volume and time justify a pilot.
Compare current manual work with an automated scenario and use that baseline to decide the next step.
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6. Preserve evidence without copying unnecessary data
Investigation requires preserving events, identifiers, versions, decisions, tool calls and relevant outcomes. But incident response does not justify copying sensitive information indiscriminately. Record what is necessary to reconstruct the sequence and use references when the original content already exists elsewhere.
Protect access to this evidence. Incident logs may contain more sensitive detail than normal dashboards, so specific permissions, defined retention and access auditing are appropriate. Traceability should improve control, not create a new risk surface.
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7. Reconstruct the case timeline
Use the correlation identifier to order intake, data retrieval, decisions, approvals, tools, errors, retries and confirmed effects. The timeline helps separate cause, symptom and consequence and prevents relying only on the last alert received.
Also include configuration changes and releases close to the incident time. Knowing which version was active and what changed recently reduces the search space and helps decide whether to roll back, contain or correct directly.
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8. Separate technical impact from business impact
An error can affect thousands of calls without changing any business data, while one incorrect action can have major financial or reputational impact. Evaluate both dimensions separately when assigning severity and prioritising recovery.
Measure how many cases, customers, organisations, documents or transactions are affected and what actual consequences occurred. This information guides communication, remediation and decisions about reopening processes, replaying actions or contacting users.
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9. Define internal and external communication
During an incident, operations needs concrete instructions; leadership needs impact and progress; customer support may need consistent messaging; security or compliance may need specific detail. Prepare templates and owners so teams are not writing from scratch under pressure.
External communication should be proportionate and based on confirmed facts. Avoid speculating about cause before investigation is complete. Explain which service is affected, what alternative exists and when the next update will be provided when appropriate.
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10. Restore the minimum safe service first
Recovery does not always require immediately solving the root cause. You can first restore a known version, disable a problematic function or route cases to human review. Then investigate and correct with more time without keeping the business blocked.
Define what minimum safe service means for each process. In support it may mean drafting without sending; in administration, reading data but not writing; in invoicing, preparing documents without issuing them. This definition speeds decisions during recovery.
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11. Verify before returning to normal autonomy
After applying a correction, test representative cases and exceptions before restoring full automation. Check integrations, permissions, policies, traces and metrics. A rushed recovery can create a second incident and complicate diagnosis.
Restore autonomy in stages where possible. Start with sampling, human approval or a limited percentage and observe behaviour for a defined window. Expand only when signals return to acceptable ranges.
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12. Document root cause and contributing factors
Do not stop at finding the component that failed. Ask why the failure reached production, why it was not detected earlier and why the impact was what it was. There may be one technical cause and several contributing factors: missing tests, broad permissions, weak alerts or slow rollback.
A useful review avoids blame and focuses on improving the system. Describe facts, decisions and barriers that worked or failed. This produces concrete actions that reduce the likelihood or impact of similar incidents.
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13. Turn every incident into verifiable improvements
Follow-up actions should have an owner, date and closure criterion. Avoid vague conclusions such as improve monitoring. It is better to define adding an alert for a specific condition, restricting a permission, creating a regression test or reducing target rollback time.
Then verify that the improvement works. Run simulations, tests or periodic reviews. An action marked complete without checking its effect can leave the same weakness hidden until the next incident.
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14. Practise before you need it
Run simple exercises: a connector returns invalid data, a credential expires, a version increases errors or a write action must be blocked. Check whether the team knows who decides, how to activate safe mode and where to find evidence.
Exercises reveal dependencies that documentation does not show: missing access, alerts that do not arrive, rollback that is too slow or owners who are unavailable. Fixing these frictions in a drill is far cheaper than discovering them during a real incident.
DESIGN A PILOT
Turn these rules into a controlled pilot with your data, systems and owners.
We can define scope, integrations, human approvals and the metrics that will show whether automation should expand.
WORKFLOW
AI Employee incident-response cycle
Assign a coordinator plus technical and business owners.
Contain scope by tool, process, tenant or autonomy level.
Activate safe mode or rollback where necessary.
Preserve evidence and reconstruct the timeline.
Evaluate technical and business impact separately.
Restore the minimum safe service.
Validate the fix and restore autonomy gradually.
Document root cause and contributing factors.
Turn findings into verifiable actions and practise the procedure.
METRICS
What to measure
Time to detection
Time to containment
Time to recovery
Cases and organisations affected
Actions reverted or corrected
Incidents by cause and severity
Share recovered through rollback
Time spent in safe mode
Recurrence by root cause
Post-incident actions completed and verified
RELATED GUIDE
How to build an incident response playbook for AI systems
A practical guide to turning incident response into a testable procedure with owners, severity, containment, recovery and learning.
TOPIC CONNECTIONS
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FAQ
Frequently asked questions
What is an incident in an AI Employee?
It is an event that materially affects or may affect availability, data, security, compliance, business outcomes or operational control and requires a coordinated response.
Do you need to shut down the AI Employee for every problem?
No. The best response is usually granular: block one tool, disable writes, reduce autonomy or isolate one process while the rest keeps working.
What is safe mode?
It is an operating state with reduced capability and lower risk, for example read-only access, draft generation or actions subject to human approval.
When should you roll back?
When evidence links a recent change to degradation and returning to a known version can restore stability faster than fixing it live.
What information should be preserved during an incident?
Relevant events, identifiers, versions, decisions, tools, outcomes and changes, while applying data minimisation and appropriate access controls.
How do you prevent the same incident from happening again?
Through root-cause analysis, concrete actions with owners and closure criteria, regression tests, alert improvements and exercises that verify the new safeguards work.
NEXT STEP
Turn this approach into a concrete proposal for your business.
We review the process, required integrations and the scope of a first pilot with you before proposing a broader implementation.