AI & RAG

Have we seen this before? Similar-incident finder on Sentry alerts

When a new Sentry issue fires, semantically searches your postmortem corpus for past incidents with the same fingerprint and posts the top matches with their root causes…

CategoryAI & RAG
Enginesim
Difficultyintermediate
Triggerevent
Steps5
Setup~15 min

How it runs

The automated pipeline, trigger to output.

  • TriggerNew Sentry issue createdSentrySentry
  • ActionEmbed error signature and vector-search postmortem corpusPostgreSQLPostgres
  • LogicFilter to matches above similarity threshold
  • ActionGround a similar-incident brief on retrieved postmortemsOpenAI
  • OutputPost ranked matches to on-call Slack channelSlack

What it does

The moment a new Sentry issue is created, this workflow checks whether your team has fought this fire before. It embeds the error signature, runs a vector search over your indexed postmortem corpus, and drops a ranked shortlist of the most similar past incidents — each with its root cause, resolution, and a link to the full writeup — straight into the on-call Slack channel.

When to use it

When recurring or look-alike incidents waste on-call time rediscovering known fixes. Use it if you keep postmortems but nobody reads them during an active page.

How it works

  1. 1A new Sentry issue triggers the flow with its title, culprit, and stack trace.
  2. 2The error text is embedded and matched against your postmortem vector store in Postgres.
  3. 3A relevance gate drops weak matches so only genuinely similar incidents pass.
  4. 4An LLM grounds a short brief strictly on the retrieved postmortems — no invented fixes.
  5. 5The ranked matches and brief are posted to the on-call Slack channel as a threaded message.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect SentryErrors, performance, releases.
  2. 2
    Connect PostgresAny Postgres URL — query, write, migrate.
  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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