Your best next flow is already in your ticket history.
AutoFlow is how we implement and continuously improve Varex AI support for Zendesk. Your team has already shown how support works: in the questions they ask, the actions they take and the replies they send. Zendesk AutoFlow uses that history to build your starting flow, so you can review something concrete instead of writing every step from scratch.
It automatically imports your Zendesk tickets, uses agents' historical replies and actions to create a candidate flow, then tests it. A candidate that passes moves into an A/B comparison with the current flow. AutoFlow improves the new version and switches only when it is reliably better.
From ticket history to a proven improvement.
One clear sequence. The current flow stays the reference until the new version earns its place.
A is the current flow. B is the candidate. If B is not reliably better, A stays in place.
Start with real tickets, not a blank specification.
In a manually designed automation project, much of the early work is explaining the process: documenting ticket types, mapping branches and translating the rules into code. Your Zendesk history already contains examples of that work.
AutoFlow imports the tickets, replies and actions automatically and uses them to generate a candidate flow. Your team can spend its time checking how the flow handles real cases rather than reconstructing the whole process for a developer.
That removes a layer of manual flow design and gives integration work a concrete starting point. Account connections, permissions and business rules still need to be set up; the difference is that the support logic does not have to begin from zero.
Past responses are evidence, not perfect instructions. Testing checks the candidate against your current policies, permitted actions and handoff requirements.
Test first. Compare second.
Check the candidate before live testing
The generated flow is tested on historical cases before it enters a live comparison. Incorrect actions and missing handoffs can be found while the work is still a candidate, when correcting them does not mean asking customers to repeat themselves.
Run current and candidate side by side
When the checks pass, AutoFlow introduces the new flow into an A/B test: the existing approach is the baseline, and the candidate is the alternative. Both are judged against the same agreed success criteria.
Improve the new version
AutoFlow uses the comparison to improve the candidate's workflow and conversation strategy. You get a path from existing tickets to tested improvements, rather than a one-off flow that needs a fresh specification each time the process changes.
Better for the customer. Better for the business.
A shorter conversation is not necessarily a better resolution. AutoFlow compares the measures that matter together:
- Action correctness and policy compliance: was the allowed action completed correctly?
- Customer satisfaction: did the interaction help the customer?
- Cancellations and refunds: did the approach improve outcomes while respecting the customer's decision and eligibility?
- Resolution and human effort: was the request completed, or did it create more work and another contact?
Your success criteria define what “better” means. An improvement in one measure should not hide unacceptable quality or customer outcomes elsewhere.
Switch when the improvement holds up.
AutoFlow switches to the new flow when the comparison shows it is reliably better against the agreed criteria—not merely ahead in an early snapshot.
If the candidate is not yet better, the existing flow stays in place while the new version is refined and tested again. Your business rules, action permissions and human-handoff requirements remain the boundaries throughout.
This is improvement of the support flow and conversation strategy. It is not a claim that every customer message automatically retrains a model.
See the loop on your own support work.
Choose one recurring request from your Zendesk. In a free demo on your product, we will show how its history becomes a candidate flow, what the tests reveal and how an old-versus-new comparison would work. You can judge the approach before scoping a wider integration.
Please do not email personal customer data, passwords or API keys. We will agree appropriate access and examples for the demo. Production integration and rollout are scoped separately.