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Find out if a release drove up Zendesk tickets
For support and engineering teams who want to know whether a release raised support load, and which Bugsnag error drove it.
On this page
Connector
Connect BugSnag, pick your AI tool and paste the prompt.
The problem
Ticket spikes after a release, and no one knows why
When support volume jumps after a release, you want to know whether the release caused it. That means comparing tickets before and after and matching them to new Bugsnag errors, which nobody has time for mid incident.
Without PopdockAI
- Count Zendesk tickets before and after the release
- Find the errors the release introduced in Bugsnag
- Read tickets to see if they match
- Draw conclusions from tiny samples
With PopdockAI
- The busiest errors first seen on the version are read
- Tickets are counted either side by type and priority
- Tickets are tied to errors by wording and conversation
- A verdict without small sample percentages, read-only
The prompt
Use this prompt
Copy it into any AI tool connected to PopdockAI. Swap the values in braces for your own.
**Objective:** Measure the support impact of release `{app-version}` of Bugsnag project `{project-name}`, comparing Zendesk tickets in the `{window-days}` days (default 7) either side of the release.
**Step 1: Find the release**
Call `bugsnag-get-release` with `project-name` set to `{project-name}` and `app-version` set to `{app-version}`, filtering `release-stage` to production. If `{app-version}` is empty, call it with `project-name` only and take the most recent production release by release-date. If nothing comes back, list the project's recent releases and stop.
Note the release-date, errors-introduced-count, errors-seen-count and unhandled-sessions-count.
**Step 2: Find what the release broke**
Call `bugsnag-get-event` with `app-version` set to the release's app-version and `received.from` set to the release-date. Group the events by error-id and keep the errors with the most events (up to 10). For each, call `bugsnag-get-error` with that error-id and keep the errors whose first-seen-date is on or after the release-date; those are new in this release.
**Step 3: Compare ticket volume**
Call `zendesk-get-ticket` twice: once with `created.from` = release-date minus `{window-days}` days and `created.to` = the day before the release-date, and once with `created.from` = release-date and `created.to` = release-date plus `{window-days}` days. Count tickets in each window, overall and by type and priority.
**Step 4: Tie tickets to new errors**
For each new error from Step 2, call `zendesk-get-ticket` with `subject.contains` set to a short fragment of its message and the after window's dates. Confirm likely matches with `zendesk-get-ticket-comment`.
**Step 5: Report**
* The release: app-version, release-date, errors introduced, unhandled sessions.
* Ticket volume before vs after, with the change as a number and a percentage.
* A table of new errors: error-id, error-class, message (trimmed), events, users, and the ticket-ids linked to it.
* A verdict: did this release measurably increase support load, and which error drove it. If volume rose but no ticket ties to a new error, say the increase is unexplained by Bugsnag.
**Rules & Guidelines:**
* `bugsnag-get-release`, `bugsnag-get-event` and `bugsnag-get-error` are on the bugsnag server. `zendesk-get-ticket` and `zendesk-get-ticket-comment` are on the zendesk server.
* This prompt is read-only. Do NOT call any add, update, assign, fix, ignore, discard or open tool on either server.
* Zendesk tickets are not tagged with app versions, so the link is by timing and wording only. Say so in the verdict.
**Validation:**
* If the after window is still in the future, compare only the days that have passed and scale the before window to the same length.
* If either window has fewer than 5 tickets, report the counts but do not draw a percentage conclusion.
* Pass dates as YYYY-MM-DD.Who it's for
One answer, three teams that use it
Support managers
Explain a ticket spike
Find out whether a release drove the jump in tickets and which error is behind it.
Did release 3.8.0 of Fabrikam Checkout raise our Zendesk tickets? Compare 5 days either side.
Product managers
Measure a release's customer impact
See the change in ticket volume by type and priority after a launch.
How did Contoso Storefront 4.3.0 affect Zendesk ticket volume over 14 days?
Engineering leads
Fix the error customers feel
Know which new error drives tickets so you fix the one that cuts support load.
Which Bugsnag error from Northwind Web 2.1.4 is driving Zendesk tickets?
How it works
What PopdockAI does when you ask
You name the project, version and window, and your AI assistant compares support load either side of the release. It reads only and says the link is by timing and wording.
01
Ask
You name the Bugsnag project, app version and a window in days.
02
Read
It finds the busiest errors first seen on that version.
03
Compare
It counts Zendesk tickets before and after, by type and priority.
04
Report
You get the change and a verdict on which error drove it.
What PopdockAI provides
The tools behind the answer
Ready made tools for BugSnag and one secure connection to your AI tool. No code, no exports.
bugsnag-get-release
bugsnag-get-event
bugsnag-get-error
zendesk-get-ticket
zendesk-get-ticket-comment
This report is built from Get data tools only. PopdockAI applies the row, field and parameter rules your admin sets, and every query is written to the audit trail.
The outcome
Know if a release raised support load
Related use cases
More ways to put this data to work
One prompt template with infinite possibilities.
Your AI assistant adapts its approach, tone, and capabilities to match each unique context while you maintain a single, manageable workflow.
Join forward-thinking companies that are transforming their operations with intelligent, secure AI automation.

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