AI & RAG

PagerDuty Incident Spec-Context Briefing

When a PagerDuty incident triggers, retrieves the relevant Dropbox specs and runbooks for the affected service and posts a grounded incident briefing into the incident's Slack…

CategoryAI & RAG
Enginesim
Difficultyadvanced
Triggerevent
Steps5
Setup~25 min

How it runs

The automated pipeline, trigger to output.

  • TriggerPagerDuty incident triggeredPagerDutyPagerDuty
  • ActionVector-search spec index by service and alert summaryPostgreSQLPostgres
  • ActionRetrieve matching spec and runbook passagesDropboxDropbox
  • ActionSynthesize grounded incident briefingOpenAI
  • OutputPost briefing with sources to incident Slack channelSlack

What it does

Gives responders instant context the moment an incident opens. It reads the firing PagerDuty alert, pulls the matching engineering specs and runbooks from the Dropbox corpus, and posts a grounded briefing covering what the service does, known failure modes, and documented rollback steps into the incident Slack channel.

When to use it

Use it to cut mean-time-to-context during incidents. Instead of a responder scrambling to find the right design doc, the relevant spec excerpts arrive automatically, scoped to the alerting service.

How it works

  1. 1A PagerDuty incident trigger fires with the alert payload.
  2. 2The service name and alert summary are used to vector-search the spec index in Postgres.
  3. 3Matching spec and runbook passages are retrieved from Dropbox with their paths.
  4. 4OpenAI synthesizes a tight incident briefing strictly from those passages.
  5. 5The briefing, with linked sources, is posted to the incident's Slack channel.

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 DropboxFiles and folders.
  4. 4
    Connect OpenAIModels, embeddings, files.
  5. 5
    Connect SlackChannels, DMs, threads, mentions.
  6. 6
    Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
  7. 7
    Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
  8. 8
    Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.

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