AI AGENTS
Sentry Error-Cluster Root-Cause Agent to Linear Bug
When a Sentry issue crosses an event threshold, an agent reads the stack trace, pulls the offending source from GitHub, diagnoses the likely cause.
How it runs
The automated pipeline, trigger to output.
- TriggerSentry issue crosses event thresholdSentry
- LogicFilter out linked or low-severity issues
- ActionFetch event, stack trace, and breadcrumbs from SentrySentry
- ActionPull implicated source files and recent commits from GitHubGitHub
- ActionDiagnose root cause and draft repro planOpenAI
- OutputOpen Linear bug with diagnosis and repro stepsLinear
What it does
Turns a noisy Sentry issue into an actionable, well-scoped Linear bug. When an issue spikes past a chosen event count, an agent inspects the stack trace, fetches the implicated source files from GitHub, reasons about the probable root cause, and files a Linear ticket that includes a hypothesis and a step-by-step reproduction plan.
When to use it
Use it when on-call engineers waste time triaging raw Sentry alerts that arrive without context. It is best for teams that route bugs through Linear and want each ticket to land with a repro plan already drafted, not just a stack trace pasted in.
How it works
- 1A Sentry issue alert fires once its event count crosses the configured threshold.
- 2A filter drops issues already linked to a ticket or below the severity bar.
- 3The agent fetches the full event payload, stack trace, and breadcrumbs from Sentry.
- 4It pulls the referenced source files and recent commits from GitHub for context.
- 5An OpenAI model ranks likely causes and drafts a numbered reproduction plan.
- 6A Linear bug is created with the diagnosis, repro steps, and a back-link to Sentry.
Set it up
What you configure once, before turning it on.
- 1Connect SentryErrors, performance, releases.
- 2Connect GitHubRepos, issues, pull requests, actions.
- 3Connect OpenAIModels, embeddings, files.
- 4Connect LinearIssues, projects, cycles, triage.
- 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.
More AI Agents workflows
Stale Doc-PR Chaser for Runbook Gaps
On a daily schedule the agent finds runbook doc PRs that were opened from resolved incidents but never reviewed, summarizes what each one fixes.
On-Call Runbook Gap Closer: Resolved Sentry Issues to Doc PRs
An agent reads each newly resolved Sentry issue, compares the actual fix against your existing runbook, and opens a GitHub PR adding the missing remediation steps.
Datadog Bill Spike Attribution Agent
When a daily Datadog cost check detects a spend jump, an agent attributes the increase to the specific services and metric types driving it and posts a ranked breakdown to Slack.
Sentry-to-Confluence Runbook Updater
When a Sentry issue is resolved, the agent finds the matching Confluence runbook page and proposes an inline update with the verified fix.
Custom Metrics Cardinality Spike Pager
A webhook from a Datadog monitor fires when custom-metric cardinality jumps; an agent pinpoints the offending metric and tag, estimates the added cost.
Resolved Incident to Public Troubleshooting Doc
For customer-facing errors resolved in Sentry, the agent drafts a sanitized troubleshooting entry and opens a PR to your ReadMe documentation.
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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