Plan a handover for a no-code AI automation

A working canvas is not a handover. The receiving owner needs to operate normal work, diagnose exceptions, control access and stop the automation safely.

Check the handover pack

The automation lab creates an authored evidence index.

from automation_cases import handover_case

result = handover_case()
assert result["complete"] is True
assert result["plain_secrets_present"] is False
assert result["acceptance_status"] == "pending"
assert result["pack"]["rollback"] == "disable trigger and drain queue"

Completeness does not mean the receiving owner accepted it, and no platform was configured.

Transfer operating knowledge

Include purpose, boundaries, trigger, workflow diagram/version, data classes, connections and vault references, permission register, schedules/rate limits, test evidence, dashboards/alerts, cost model, exception queue and recovery/rollback. List platform/vendor dependencies and renewal owners.

Run a shadow operation: the new owner handles a normal event, duplicate, approval timeout, connector failure and replay. Verify they can pause intake, drain or preserve in-flight work, rotate a connection and find audit evidence without the builder.

Record known risks, open defects, service objective, contacts and change process. Remove builder access when appropriate only after acceptance and credential ownership transfer. Never place plaintext secrets in screenshots or exported workflow JSON.

The Generative & Agentic AI course treats runbooks and ownership as required deployment artifacts.

Exercise

Create a handover pack for a local fixture workflow and ask a peer to recover one injected failure using only the pack. Log every missing instruction and retest.

Continue learning

This article is part of the Business automation with AI sequence. Use the neighbouring tasks when you need the prerequisite or the next application.

• Review the prerequisite or neighbouring task in Document permissions for a business automation owner.
• Continue with Portfolio project: an evaluated document-to-decision workflow.

Reference: Make incomplete executions.