AI Workflow Fallback Testing: Lessons from Google Cloud.

What can the Google Cloud outage teach us about AI workflow fallbacks?

On 20 October 2026, Google Cloud experienced disruption in its London region, europe-west2. Its incident report, published on 27 October, says the primary impact was mitigated after six hours and eight minutes. Some workloads had a longer recovery tail.

This article uses the later incident report, checked on 1 November 2026. Earlier updates on the same status page contain preliminary timings. The reported cause concerns infrastructure and power controls; it is not described as an AI-caused outage.

The wider business relevance is clear: on 20 October, the UK Treasury Committee said its Chair had written to the Bank of England and Financial Conduct Authority about the outage affecting organisations including banks.

• Google Cloud: London incident report — outage 20 October, report 27 October 2026 ↗
• UK Treasury Committee: Google Cloud outage letter — 20 October 2026 ↗

What went wrong: a backup existed, but the conditions mattered

Google reports that an external power fault occurred while one supply path was isolated for maintenance. Backup controls detected voltage on that disconnected path and did not start the generators. Batteries eventually ran out. Redirected workloads then contributed to capacity pressure and delays elsewhere in the region.

Our takeaway for AI workflows is to test the conditions under which a fallback is expected to work. A second connection is only useful if it is available, has the required capacity and can complete the intended task when the first connection fails.

This is a lesson drawn from the report, not a claim that VisiFlo could have prevented the power failure or has assessed Google’s infrastructure.

• Google Cloud: London incident report — outage 20 October, report 27 October 2026 ↗

Your AI can sound helpful while the business task is stuck

A customer-facing assistant may depend on several services: one to find the customer, another to check availability and another to create a booking. The conversation can continue even when one of those services stops responding.

That leaves several possible states. Nothing may have happened. The booking may have been created but the confirmation lost. The result may still be pending. Treating every timeout as a failed action can lead to duplicate work; treating it as success can mislead the customer.

The following diagram is a fictional workflow example. It illustrates a test question for an AI integration, rather than behaviour observed during the Google incident.

A practical example: did the appointment get booked?

Imagine a fictional appointment assistant. A customer asks for a Tuesday slot. The booking service creates the appointment, but the response is delayed until the assistant’s waiting time expires.

If the workflow immediately submits the same request again, it could create another appointment unless the booking system prevents duplicates. If it tells the customer the first request failed, the customer may also try again. Neither response establishes what actually happened.

A better expected behaviour is agreed before testing: keep the request reference, check the booking status where supported, and avoid another write while the outcome remains unknown. If the status cannot be established, tell the customer it is being checked and pass the case to the named owner.

Test that situation alongside a request that never reached the booking service and one that is still processing. Those cases look similar from a chat window, but may require different next steps. This is a proposed test design using fictional records, not an executed assessment.

A fallback needs to preserve the business rules

A backup should do more than return a response. It needs to preserve the rules that matter: the same customer, correct source information, required approval and no unintended duplicate actions.

An alternative may also depend on the same database, account or cloud location as the primary service. Map those shared dependencies before treating two connections as separate recovery options.

If the fallback uses a different AI model, check its answers and actions against the same requirements. Confirm that sending information to the alternative service is authorised. A backup that changes the business outcome or receives data it should not receive is not ready for use.

• Can the workflow distinguish a slow response from a confirmed rejection?
• Is there an agreed limit on how long to wait and how many attempts to make?
• Does a retry preserve the request identity, and does the receiving service actually prevent duplicates?
• Can the fallback work without the dependency being simulated as unavailable?
• Does the user receive an honest status when the final outcome is unknown?
• Can a person find and resolve pending work without accidentally repeating it?

How to test this without disrupting real customers

Use an authorised test environment with fictional records and simulated service responses. The starting point is one business task and a written expectation for each failure condition. There is no need to cause a cloud outage or send test actions to customers.

1. 1. Map the dependencies

   List the services needed to complete the task. Include the model, records, external tools and the person who owns unresolved work.

2. 2. Introduce controlled failures

   Simulate a slow response, an unavailable service, a lost confirmation after a completed action and an unavailable fallback. Keep the outcomes observable.

3. 3. Inspect the business result

   Check the final record, number of writes, messages sent, repeated attempts and pending cases. Verify the evidence behind “done”, “failed” or “still checking”.

4. 4. Restore and clear the backlog

   Bring the simulated service back, reconcile pending work and confirm each task completes only as intended. Check that normal work still succeeds after improvements.

What useful evidence should show

For the appointment example, a clear report would show the request reference, the point at which the response was lost, whether a booking existed, whether another write occurred and what the customer was told.

Record the workflow version, test conditions, attempts, observed outcomes and any gaps in visibility. Agree timing and retry limits with the business rather than presenting one number as suitable for every task.

Keep incomplete outcomes visible. “We could not establish whether the booking exists” is a finding that needs attention. It should not disappear into a successful-run percentage. A good connection status also does not prove that pending business work has been reconciled.

How VisiFlo helps make the fallback testable

VisiFlo helps businesses verify and validate what an AI workflow actually does, including the awkward moments when a connected service slows down or stops responding.

A scoped review can cover n8n automations, tool-using assistants, custom agents and API-connected workflows. We agree the task, access and expected behaviour, then identify relevant checks, explain the evidence and recommend improvements. Remediation and retesting can be scoped with the system owners.

For infrastructure availability and capacity, work with the relevant cloud and operations specialists. VisiFlo’s workflow testing complements that work by asking whether the business task behaves as intended when a dependency fails. We have not assessed Google Cloud or the organisations affected by this incident.

Bring one workflow and one failure you are worried about. The useful result is a clear picture of what continues, what pauses, what needs a person and how you know the final outcome.