TICKET MANAGEMENT
Match a Zendesk regression to its Sentry errors and enrich the GitHub issue
When a regression ticket comes in, an agent searches Sentry for matching error events by user email and timeframe, attaches the stack traces and event counts.
How it runs
The automated pipeline, trigger to output.
- TriggerZendesk ticket tagged 'regression-error'Zendesk
- ActionExtract customer email, time window, and versionOpenAI
- ActionSearch Sentry for matching error eventsSentry
- LogicBranch on whether matching events were found
- ActionCreate or update GitHub issue with stack tracesGitHub
- OutputWrite GitHub link and match summary to ZendeskZendesk
What it does
Connects the human bug report to the machine evidence. The agent takes a Zendesk regression ticket, searches Sentry for error events from that customer's account around the reported time, and bundles the matching stack traces, frequency, and affected release into a GitHub issue. Engineers get the customer story and the exception side by side.
When to use it
Use it for crash- or error-class regressions where the customer says "it broke" and you want the actual exception and release that introduced it, not a guess.
How it works
- 1A Zendesk ticket tagged `regression-error` triggers the run.
- 2The agent extracts the customer email, reported time window, and app version from the ticket.
- 3It queries Sentry for matching error events scoped to that user and window.
- 4A logic step branches: if matching events are found, it enriches with stack traces and counts; if none, it flags the ticket as "no telemetry match" for manual review.
- 5The agent creates or updates a GitHub issue with the Sentry permalinks and repro steps.
- 6The GitHub link and a match summary are written back to the Zendesk ticket.
Set it up
What you configure once, before turning it on.
- 1Connect ZendeskTickets, queues, knowledge base.
- 2Connect SentryErrors, performance, releases.
- 3Connect GitHubRepos, issues, pull requests, actions.
- 4Connect OpenAIModels, embeddings, files.
- 5Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
- 6Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
- 7Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.
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Run it inside a business
This workflow drops into a full company template. Import the org, and this is one of the playbooks its agents run.
Run this workflow in your colony.
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