The easy part of an AI support agent is the ticket it can resolve outright — a password reset, a status lookup, a question already answered in the docs. The design decisions that actually matter show up in the tickets it can't close on its own, and what happens next.
Resolve on policy, not on confidence
STIV Support resolves tickets against the resolution policy your team defines — a specific, bounded set of request types with a known-correct answer. It doesn't resolve a ticket because the agent seems confident about the answer; it resolves a ticket because that request type is on the list of things it's been authorized to close without a human. That distinction matters, because a model's confidence is not an audit trail, and a policy list is.
What a handoff actually carries
When a ticket falls outside that policy — a billing dispute, anything touching an active account issue, a customer who's clearly frustrated — it hands off. The point of building this as a real handoff rather than a rejection is what travels with it:
- The full ticket history and any prior related tickets from the same customer, so the person picking it up isn't starting from zero.
- What STIV Support already checked and ruled out, so a human isn't repeating diagnostic steps that already happened.
- A plain note on why it escalated — not just that it did — so the reviewer knows what to look at first.
- No re-explaining by the customer. The context moves with the ticket, not the other way around.
Watching for the pattern, not just the ticket
A single ticket is a data point. STIV Support also watches across tickets for the same underlying issue showing up repeatedly — a bug, a confusing product change, a documentation gap — and flags that pattern instead of treating each instance as an isolated case to close. That's the difference between a support agent that clears a queue and one that helps a team stop generating the same queue every week.
The measure of a support agent isn't how many tickets it closes on its own — it's whether the ones it hands off arrive better-prepared than they would have if a human had triaged them cold. Response times staying consistent during a volume spike is a side effect of that design, not the goal itself.