DEVOPS

Sentry Error Spike to Suspect-Deploy Correlation

When Sentry detects a new error spike, find the GitHub deploy that likely caused it, summarize the regression with an LLM, and post a candidate-cause report to Slack.

CategoryDevOps
Enginesim
Difficultyadvanced
Triggerevent
Steps5
Setup~25 min

How it runs

The automated pipeline, trigger to output.

  • TriggerSentry error spike alertSentrySentry
  • ActionRead issue first-seen, stack trace, and releaseSentrySentry
  • ActionFetch GitHub deploys and PRs before the spikeGitHubGitHub
  • LogicRank suspect commits and summarize regression with LLMOpenAI
  • OutputPost suspect-cause report to SlackSlack

What it does

Correlates a fresh error spike in Sentry with the most recent code that shipped, so responders start triage with a prime suspect instead of a blank page. It pulls the failing issue's first-seen timestamp, finds the GitHub deployments and merged PRs just before it, asks an LLM to summarize the likely regression, and posts a ranked suspect-commit report to Slack.

When to use it

Use it when error spikes in production are hard to trace back to a release, and you want an automatic 'what changed right before this broke' brief on every new Sentry issue.

How it works

  1. 1Sentry fires an alert for a new or regressed issue crossing its event threshold.
  2. 2The flow reads the issue's first-seen time, stack trace, and affected release.
  3. 3GitHub is queried for deployments and PRs merged in the window just before first-seen.
  4. 4An LLM ranks the suspect commits by how well their changed files match the failing stack frames and writes a plain-English cause summary.
  5. 5A Slack message posts the suspect ranking, the regression summary, and direct links to the issue and each candidate PR.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect SentryErrors, performance, releases.
  2. 2
    Connect GitHubRepos, issues, pull requests, actions.
  3. 3
    Connect OpenAIModels, embeddings, files.
  4. 4
    Connect SlackChannels, DMs, threads, mentions.
  5. 5
    Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
  6. 6
    Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
  7. 7
    Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.

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