4 Real Support Scenarios — And How Frank Handled Each One

Theory is one thing. Watching an AI agent handle real tickets from your production support desk is another. Here are four scenarios from Rapax’s own support queue — and exactly how Frank responded.

By Shawn Ennis•June 22, 2026•6 min read

In the last post, I walked through the five decisions Frank makes on every ticket. In this one, I want to show you what those decisions look like in practice. These are scenarios from Rapax’s production support desk, with the customer details anonymized but the responses unchanged.

If you’ve been in telecom support for any length of time, you’ll recognize all four of these.

Scenario 1: The KB question that never reached a human

The ticket: “Which alert fields are used for Up/Down correlation in core-correlation?”

A customer engineer was trying to understand how the alert correlation engine determined whether two alerts were related. Specifically, which fields the correlation engine used to decide Up/Down state, and how parent and child alerts linked.

What Frank did:

1. Frank classified the ticket as type=question with 96% confidence.
2. Frank queried Wade, which returned a HIGH confidence match to an existing KB article on correlation field semantics.
3. Frank auto-closed the ticket with the KB answer posted as a public note, including the article link.
4. The customer received the answer within 30 seconds of submitting the ticket.

Engineer time spent: Zero. Time to resolution: 28 seconds. Customer outcome: They got the answer faster than I would have.

This is the highest-leverage thing Frank does. Multiply this by 40-50% of incoming tickets and you understand where the deflection savings come from.

Scenario 2: The bug report sent back for missing logs

The ticket: “SNMP poller crashing with ‘str object has no attribute get’”

A customer reported their SNMP poller was throwing errors continuously and 47 devices were no longer being polled. They included one error line and asked what to do.

What Frank did:

1. Frank classified the ticket as type=bug with 91% confidence.
2. Frank ran the bug intake checklist. Required fields: Rapax version, OS/distribution, full error log (at least 50 lines around the error), affected device count and types, when the issue started.
3. Frank found three of five fields missing. The ticket described the error but didn’t include the full log, version, or OS.
4. Frank posted a note back to the customer listing exactly what was needed and put the ticket into “pending information” state.

When the customer replied with the full log and version info, Frank re-classified, then escalated to the on-call engineer with a complete intake. The engineer who picked it up had everything they needed to start debugging immediately.

Compare to the old workflow: Without Frank, an engineer would have read the ticket, sent an email asking for the missing info, waited a day for the response, then started debugging. Frank compressed three days of back-and-forth into one structured request.

Scenario 3: Two “App is Broken” tickets, linked automatically

The tickets: Both subject lines were literally “App is Broken.” Both descriptions were “Not sure why but it is not working right now.” Both filed from the same company within 45 minutes of each other.

Vague reports happen. The frustration is real, the information is none.

What Frank did:

1. Frank classified both tickets as type=bug.
2. On the second ticket, Frank ran duplicate detection — comparing subject and description similarity against open tickets from the same company filed in the last 24 hours.
3. Frank found the match and linked the two tickets in the related_incidents[] field. He posted a private internal note flagging the duplicate.
4. Frank then ran intake validation on both. Both were missing reproduction steps, affected systems, and screenshots/errors. Frank asked the customer for those details once — knowing the answers would apply to both tickets.

When the customer responded, Frank had context for both tickets. The engineer who eventually picked up the work saw a unified picture instead of fragmented complaints.

Scenario 4: The legal threat detected at midnight

The ticket: Here is an extreme example I ran by the team. A customer who hadn’t received a response for 72 hours sent a follow-up. The subject line was “Rapax Broken – demanding response.” The body included “I am going to turn this off” and “I expect a response within the hour.”

This kind of ticket happens does happen. SLA breaches happen. The question is what your system does when it happens.

What Frank did:

1. Frank detected escalation language patterns: legal threats, response demands, time-pressure language (“within the hour”), and aggressive subject line.
2. Frank ran frustration analysis and flagged the customer state as “high escalation risk.”
3. Frank immediately paged the on-call manager via Grace (our escalation paging system) — not the next morning, not after another email exchange. Within seconds of the ticket being filed.
4. Frank posted a public note to the customer acknowledging the urgency and confirming a senior engineer had been engaged.
5. Frank attached the full ticket history and original missed ticket to the escalation note so the on-call manager had context immediately.

Without Frank, this ticket might have sat in the queue for another 12 hours. By the time a human saw it, the customer would be calling their lawyer. Frank’s frustration detection turned a potential SLA disaster into a 90-second response.

The pattern across all four

Each of these scenarios played out in production on the Rapax support desk. They’re not optimized demos. They’re the kinds of tickets your team probably sees every week.

The common thread: Frank handled the administrative work in seconds. Either by resolving it entirely (Scenario 1), pre-processing it for a human (Scenarios 2 and 3), or flagging urgency that humans missed (Scenario 4).

Your engineers’ time gets spent on the 15-25% that actually requires their judgment. Frank handles the rest.

In the next post, I’ll walk through the actual ROI math — what these scenarios add up to in dollars saved, payback period, and FTEs deferred.

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About the author Shawn Ennis is the Founder & CEO of Rapax and Citus Technologies. With 25+ years in telecom operations, Shawn previously founded Assure1 (acquired by Oracle in 2021), holds 12 patents in telecom OSS/BSS, and hosts the Transformation Leaders Podcast. Connect on LinkedIn.