ENGINEERING
Customer-reported error to Sentry correlation and GitHub bug
When a support ticket mentions an error or crash, it searches Sentry for matching events around the customer's timestamp, and if a real issue is found.
How it runs
The automated pipeline, trigger to output.
- TriggerFront conversation mentioning an error/crash arrivesFront
- ActionLLM extracts error signature, feature, and timestampOpenAI
- ActionSearch Sentry for matching events near that timeSentry
- LogicProceed only if a credible Sentry match exists
- ActionFile a GitHub bug linking ticket, customer, and Sentry issueGitHub
- OutputUpdate the Front conversation with the GitHub issue linkFront
What it does
Connects the support inbox to your error tracker: when a customer reports something broke, it finds the matching Sentry events and turns a vague complaint into a concrete, reproducible GitHub bug with real telemetry attached.
When to use it
Use it when support and engineering live in different tools and customer-reported errors get lost in translation. Best for teams on Front for support who want every credible crash report verified against Sentry before it becomes engineering work.
How it works
- 1A new or tagged Front conversation mentioning an error or crash triggers the flow.
- 2An LLM step extracts the likely error signature, affected feature, and customer timestamp from the message.
- 3The flow searches Sentry for matching events near that timestamp and customer.
- 4A logic step proceeds only when a credible matching issue is found, otherwise it leaves a note for the support agent.
- 5A GitHub bug is filed linking the Front conversation, the customer, and the Sentry issue with reproduction context.
- 6The Front conversation is updated with the GitHub issue link so support can close the loop with the customer.
Set it up
What you configure once, before turning it on.
- 1Connect FrontShared inbox, conversations.
- 2Connect OpenAIModels, embeddings, files.
- 3Connect SentryErrors, performance, releases.
- 4Connect GitHubRepos, issues, pull requests, actions.
- 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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