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Retirement Scenario Guide

Guide Maps

graph LR
  activate["Activate rule"] --> alert["Trigger alert"]
  alert --> inspect["Inspect open incident"]
  inspect --> retire["Retire rule"]
  retire --> review["Review cleared derived state"]
flowchart TD
  setup["Read the fixed retirement setup"] --> before["Inspect the active snapshot"]
  before --> after["Inspect the retired snapshot"]
  after --> explain["Explain what stayed authoritative and what was cleared downstream"]

Use this guide when you want the capstone to demonstrate full lifecycle ownership, not just registration and activation. This local scenario shows how retirement changes both authoritative and derived state.

Fixed setup

  • policy id: service-monitoring-retirement
  • rule id: disk-hot
  • metric: disk
  • threshold: 0.85
  • severity: warning
  • sample: disk=0.91
  • retirement reason: replaced by storage saturation policy

What should happen

  1. The rule is registered and activated.
  2. One observed sample triggers an open incident.
  3. The rule is retired by the aggregate.
  4. The rule remains in the retired lifecycle state.
  5. The active-rule projection and open-incident projection are cleared for that rule.
  6. The incident history remains as a derived record of what happened before retirement.

Best local proof surfaces

  • tests/test_policy_lifecycle.py for lifecycle ownership
  • tests/test_runtime.py for projection cleanup after retirement
  • build_retirement_review() in scenario.py for the fixed local contract

Best companion guides