AI & RAG

Suggest the matching runbook when PagerDuty pages on-call

On a PagerDuty incident trigger, retrieves the most similar past incidents from your postmortem corpus and replies with a grounded runbook suggestion — likely cause.

CategoryAI & RAG
Enginesim
Difficultyintermediate
Triggerevent
Steps5
Setup~15 min

How it runs

The automated pipeline, trigger to output.

  • TriggerPagerDuty incident triggeredPagerDutyPagerDuty
  • ActionEmbed alert and vector-search postmortem corpusPostgreSQLPostgres
  • LogicGate on retrieval confidence
  • ActionCompose grounded runbook suggestion with citationsOpenAI
  • OutputAdd suggestion as a note on the PagerDuty incidentPagerDutyPagerDuty

What it does

When PagerDuty escalates an incident, this workflow turns the alert summary into a retrieval query, finds the closest historical incidents in your postmortem corpus, and writes a grounded suggestion back onto the PagerDuty incident itself: the most likely cause, the remediation that resolved the prior occurrence, and links to the source postmortems. The responder sees it the moment they acknowledge.

When to use it

When your team relies on PagerDuty for paging and you want the relevant runbook surfaced at the point of acknowledgment instead of buried in a wiki. Best for teams with a meaningful history of recurring incident classes.

How it works

  1. 1A PagerDuty incident triggers the flow with its title, service, and description.
  2. 2The alert text is embedded and vector-searched against the postmortem corpus in Postgres.
  3. 3A confidence gate decides whether matches are strong enough to surface.
  4. 4An LLM composes a runbook suggestion grounded only on retrieved postmortems, citing each.
  5. 5The suggestion is added as a note on the PagerDuty incident for the responder.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect PagerDutyIncidents, on-call, escalations.
  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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