AI AGENTS

Sentry Regression Blame to GitHub PR Comment

On a new Sentry issue, an agent maps the failing frame to the commit that introduced it and posts a blame comment on the originating GitHub pull request so the author sees it…

CategoryAI Agents
Enginesim
Difficultyadvanced
Triggerevent
Steps5
Setup~25 min

How it runs

The automated pipeline, trigger to output.

  • TriggerNew Sentry issue first seenSentrySentry
  • ActionFetch top failing frame, file, and line from SentrySentrySentry
  • ActionBlame the line and resolve the introducing PR on GitHubGitHubGitHub
  • ActionWrite a regression explanation tying error to the changeOpenAI
  • OutputComment the blame and trace on the originating PRGitHubGitHub

What it does

Connects a fresh Sentry crash back to the change that caused it. The agent reads the failing stack frame, runs git blame against GitHub to find the introducing commit, identifies the pull request that merged it, and posts a comment on that PR with the error and a short explanation — putting the regression in front of the author who wrote the code.

When to use it

Use it when regressions slip through and nobody notices which deploy broke things. It shines for teams that want accountability tied to the original PR rather than a generic alert, shortening the loop between "error in prod" and "author is looking at it."

How it works

  1. 1Sentry fires when a brand-new issue (first seen) appears.
  2. 2The agent fetches the top stack frame, file path, and line number from Sentry.
  3. 3It runs blame on that line via the GitHub API to find the introducing commit and PR.
  4. 4An OpenAI model writes a concise explanation linking the error to the code change.
  5. 5A comment is posted on the originating GitHub pull request with the trace and link.

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
    Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
  5. 5
    Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
  6. 6
    Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.

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