AI AGENTS

Weekly Alert Promotion Recommender

Each week an agent reviews which clustered Axiom log patterns correlated with real incidents and recommends which to promote into alerts and which existing alerts to retire.

CategoryAI Agents
EngineSim + Paperclip
Difficultyadvanced
Triggerschedule
Steps5
Setup~25 min

How it runs

The automated pipeline, trigger to output.

  • TriggerWeekly schedule
  • ActionPull Axiom pattern volumes and fired alertsAxiom
  • LogicScore patterns under- vs over-alerted
  • ActionLLM drafts promotion and retirement recsOpenAI
  • OutputPost Slack review and open Linear tasksLinearLinear

What it does

This agent does the meta-work of keeping alerting healthy. Weekly it correlates the prior week's log-pattern clusters in Axiom against actual incidents and on-call pages, then recommends which silent-but-meaningful patterns deserve a new alert and which noisy or never-fired alerts should be retired. The output is a tuning review, not just raw stats.

When to use it

Use it when alert fatigue has set in: pages that nobody acts on, plus real failures that never alerted. It rebalances the alert set on a steady cadence so coverage tracks reality instead of drifting.

How it works

  1. 1A weekly schedule triggers the run.
  2. 2The agent queries Axiom for clustered pattern volumes and the list of alerts that fired during the week.
  3. 3A logic step joins patterns to incidents to score each as under-alerted or over-alerted.
  4. 4An LLM step writes promotion and retirement recommendations with the proposed Axiom alert query for each.
  5. 5It posts a Slack review thread and opens Linear tasks for the accepted changes.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect AxiomLog streams, queries, dashboards.
  2. 2
    Connect SlackChannels, DMs, threads, mentions.
  3. 3
    Connect LinearIssues, projects, cycles, triage.
  4. 4
    Connect OpenAIModels, embeddings, files.
  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.

Run this workflow in your colony.

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