AI & RAG

Recurring-incident investigator agent for engineering leadership

On demand or weekly, an agent searches the postmortem corpus for clusters of similar incidents, identifies which root causes keep recurring.

CategoryAI & RAG
Enginepaperclip
Difficultyadvanced
Triggerschedule
Steps5
Setup~25 min

How it runs

The automated pipeline, trigger to output.

  • TriggerWeekly or on-demand investigation request
  • ActionRun multi-query semantic search over postmortem corpusPostgreSQLPostgres
  • LogicCluster incidents and discard coincidental matches
  • ActionDraft systemic-cause findings with per-incident evidenceOpenAI
  • OutputFile Linear issue with proposed fixLinearLinear

What it does

This is an agent-driven investigation rather than a single lookup. It mines your postmortem corpus to find clusters of incidents that share a root cause — the same flaky dependency, the same missing alert, the same config footgun — and reasons about which problems are genuinely recurring versus one-offs. It then writes up the pattern with evidence drawn from each contributing postmortem and files a Linear issue proposing the systemic fix.

When to use it

When leadership wants to stop firefighting symptoms and invest in the underlying fixes. Run it weekly for a standing reliability review, or on demand before planning.

How it works

  1. 1A schedule or manual request starts the investigator agent.
  2. 2The agent runs multiple semantic searches over the Postgres postmortem corpus to surface candidate clusters.
  3. 3It evaluates each cluster, discarding coincidental matches and keeping ones with a shared causal thread.
  4. 4For each real pattern it drafts a findings summary citing every contributing incident.
  5. 5It files a Linear issue with the proposed systemic remediation and evidence links.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect PostgresAny Postgres URL — query, write, migrate.
  2. 2
    Connect OpenAIModels, embeddings, files.
  3. 3
    Connect LinearIssues, projects, cycles, triage.
  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.

Run this workflow in your colony.

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