How to build an incident response playbook for AI Employees

An AI Employee in production is part of a distributed system: data, models, connectors, rules, permissions, queues, people and business applications. When something fails, investigating only the model's last message is usually insufficient. An incident playbook defines in advance how to classify the problem, who coordinates, which controls can reduce scope, what evidence to preserve, how to restore service and how to verify that risk has returned to an acceptable level. The goal is to reduce improvisation, recovery time and repeated mistakes.

Define levels with concrete examples. Low severity may be degradation without visible impact; medium, a process with repeated errors or threatened SLA; high, an incorrect action on critical data; critical, a security, privacy or broad-impact problem requiring immediate containment.

For each level set target times for acknowledgement, containment and updates. Also specify who must be informed. This table reduces ambiguity and prevents an important incident from being overlooked simply because it affects few technical events.

List what can be disabled without deploying code: a tool, a write action, one tenant, one automation, a provider, a queue or an autonomy level. Record where the control is located and who is authorised to use it.

Include side effects. Disabling ERP may prevent order completion; blocking email sending may leave drafts pending. Knowing the impact of each lever helps choose the minimum necessary containment.

Keep model, prompt, rule, tool, connector and knowledge versions identifiable. The playbook should explain how to return to a known combination and which dependencies need to be reverted together.

Test rollback periodically. A procedure that worked months ago may break after infrastructure or data changes. Measure how long it takes from the decision until the previous version is actually active.

The first questions should be simple: is it still happening, which processes are affected, are writes or sensitive data involved, what changed recently and is a safe path available? These answers guide containment before deep investigation begins.

Avoid changing multiple components at once during triage. Every additional change can hide the original cause. Prioritise stabilisation, record observations and test hypotheses in a controlled way.

For each severity define recipients, channel, frequency and update owner. Operations needs instructions; business needs impact; leadership needs progress; customers may need information when service or their data is affected.

Use confirmed facts and clearly separate what is known from what remains under investigation. A good update includes current status, scope, actions taken, open risks and the next review point.

Restoring the system does not automatically correct earlier effects. Identify affected records, messages, documents or transactions and decide whether they must be reverted, corrected, resent or manually reviewed.

Automate remediation only when it is safe and verifiable. In sensitive cases it may be better to generate a proposed-action list for human approval. Preserve evidence of what was corrected and with what outcome.

Every improvement should have an owner, priority and acceptance criterion. Examples include adding validation, restricting a scope, creating an alert, adding a test, reducing rollback time or documenting an external dependency.

Avoid accumulating dozens of low-priority actions. Prioritise those that significantly reduce likelihood or impact and later verify that the safeguard actually works through tests or exercises.

New tools, processes, providers and autonomy levels change containment and recovery options. Include playbook review in significant releases so owners, links and procedures do not become obsolete.

Check emergency access, rollback paths, contacts and safe mode in particular. An updated document with outdated technical controls is still insufficient. The playbook must reflect the system's real capabilities.