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Find the Bugsnag error behind a customer's Zendesk ticket
For support agents who want to hand engineers the likely application error behind a ticket, by asking their AI assistant.
On this page
Connector
Connect BugSnag, pick your AI tool and paste the prompt.
The problem
Tickets reach engineers with no technical lead
A customer says something broke and the agent needs the error behind it. Customers paraphrase error text, so searching Bugsnag with their sentence finds nothing, and engineers get tickets with no technical lead.
Without PopdockAI
- Read the whole ticket conversation
- Search Bugsnag with the customer's wording
- Find nothing because the text is paraphrased
- Pass a vague ticket to engineering
With PopdockAI
- Messages, pages, versions and timing are pulled from every comment
- Bugsnag is searched with short distinctive fragments
- Each candidate is rated high, medium or low
- A low match is never called the cause, nothing changed
The prompt
Use this prompt
Copy it into any AI tool connected to PopdockAI. Swap the values in braces for your own.
**Objective:** Find the Bugsnag errors most likely behind Zendesk ticket `{ticket-id}`, searching Bugsnag project `{project-name}` (default: all projects).
**Step 1: Read the ticket**
Call `zendesk-get-ticket` with ticket-id `{ticket-id}`. If nothing comes back, report that the ticket does not exist and stop. Note the subject, requester, organization and created-date.
Then call `zendesk-get-ticket-comment` with ticket-id `{ticket-id}` and read every comment. Pull out the evidence a Bugsnag search can use: quoted error messages, exception or class names (for example NullReferenceException, TypeError), page or screen names, URLs, app versions, and when the customer says it happened.
If the ticket carries no technical evidence at all (no message, no page, no version), say so, list what the agent should ask the customer for, and stop.
**Step 2: Search Bugsnag with that evidence**
For each distinct message or class you found, call `bugsnag-get-error` with `message.contains` or `error-class.contains` set to the shortest distinctive fragment (a few words, not the full sentence), `last-seen.from` set to 7 days before the ticket's created-date, and `project-name` set to `{project-name}` when it was given.
If the ticket names a page or URL but no message, call `bugsnag-get-event` with `received.from` and `received.to` spanning 2 days either side of when the customer says it happened (or the ticket's created-date), and look for events whose request-url or context matches that page. Use `app-version` too when the ticket gives one.
**Step 3: Rank the candidates**
For each candidate error, judge the match on: wording overlap with the ticket, timing (was the error seen around when the customer reported it), page or context match, and version match. Rate each High, Medium or Low. Drop Low matches unless nothing better exists.
**Step 4: Report**
* One line summarizing the ticket: id, subject, requester, organization, created-date.
* A table of candidate errors: error-id, error-class, message (trimmed), project-name, status, severity, events, last-seen-date, match rating, and why it matches.
* The error-url of the best match, written as plain text.
* If no candidate rated Medium or better, say plainly that no Bugsnag error matches this ticket and list the search terms you tried.
**Rules & Guidelines:**
* `zendesk-get-ticket` and `zendesk-get-ticket-comment` are on the zendesk server. `bugsnag-get-error` and `bugsnag-get-event` are on the bugsnag server.
* This prompt is read-only. Do NOT call `zendesk-update-ticket`, `zendesk-add-ticket-comment`, `bugsnag-link-error-issue`, `bugsnag-assign-error`, `bugsnag-open-error`, `bugsnag-fix-error` or any other write tool on either server.
* Search with short fragments. Customers paraphrase error text, so a full-sentence match will usually miss.
**Validation:**
* If Bugsnag returns more than 20 errors for a fragment, it is too generic. Narrow it with `error-class.contains` or a tighter date window rather than reporting all of them.
* Dates: pass dates to both servers as YYYY-MM-DD.
* Never present a Low match as the cause. Say it is a possibility.Who it's for
One answer, three teams that use it
Support agents
Give engineers a real lead
Attach the likely error to a ticket before escalating, so engineering can start straight away.
Which Bugsnag error is behind Zendesk ticket 51207 from Contoso?
Support engineers
Confirm a known issue
Check whether a customer hit an error already being worked on.
Find the Bugsnag error behind ticket 49880 in the Fabrikam Checkout project.
Escalation managers
Know what to ask the customer
When a ticket has no technical detail, get the questions that will find the cause.
Ticket 50112 from Northwind Traders says checkout broke. What Bugsnag error could it be, or what should I ask?
How it works
What PopdockAI does when you ask
You give the Zendesk ticket and your AI assistant looks for the matching Bugsnag error. It only reads, and tells you what to ask the customer when detail is missing.
01
Ask
You give the Zendesk ticket, and optionally a Bugsnag project.
02
Read
It reads every comment for messages, pages, versions and timing.
03
Search
It searches Bugsnag with short fragments around the reported time.
04
Report
You get candidates rated high, medium or low, and nothing changes.
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.
zendesk-get-ticket
zendesk-get-ticket-comment
bugsnag-get-error
bugsnag-get-event
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
Engineers get the likely error, not a vague ticket
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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