AI & RAG

Flag duplicate root causes when a new postmortem is filed

When a new postmortem is published in Confluence, searches the existing corpus for prior incidents with the same root cause and comments on the page with the matches.

CategoryAI & RAG
Enginesim
Difficultyintermediate
Triggerevent
Steps5
Setup~15 min

How it runs

The automated pipeline, trigger to output.

  • TriggerNew postmortem published in ConfluenceConfluenceConfluence
  • ActionEmbed root cause and vector-search prior incidentsPostgreSQLPostgres
  • LogicKeep related matches, exclude the page itself
  • ActionDraft grounded related-incidents noteOpenAI
  • OutputComment on the Confluence page with related historyConfluenceConfluence

What it does

This closes the loop at write time. As soon as a new postmortem page is published in Confluence, the workflow embeds its root-cause and symptom sections, searches the existing corpus for prior incidents that share the same underlying cause, and posts a comment on the new page listing the related historical incidents with links. Authors immediately see whether they are documenting a fresh problem or a repeat — and can cross-link instead of fragmenting the record.

When to use it

When postmortems live in Confluence and you want recurrence visible at authoring time, not discovered months later. It quietly improves corpus quality by encouraging cross-references.

How it works

  1. 1A new or newly published Confluence postmortem page triggers the flow.
  2. 2Its root-cause and symptom text is embedded and vector-searched against the Postgres corpus.
  3. 3A relevance gate keeps only genuinely related prior incidents and excludes the page itself.
  4. 4An LLM writes a short 'related incidents' note grounded on the matches, with links.
  5. 5The note is posted as a comment on the Confluence postmortem page.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect ConfluenceSpaces, pages, blueprints.
  2. 2
    Connect PostgresAny Postgres URL — query, write, migrate.
  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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