Test postmortems signals and thresholds

You own an AI document summarization service at POST /v1/summaries. The goal is to turn incident evidence into a blameless causal analysis with owned corrective actions that reduce recurrence and detection time. The measurable target is publish within five business days, reconcile logs and deployment events to one UTC timeline, separate trigger from contributing conditions, and assign every action an owner, due date, verification method, and risk-reduction claim. The known production tension is deep analysis finds systemic conditions but delays publication; many corrective actions feel thorough but dilute ownership and hide which controls materially reduce risk.

Convert the Postmortems objective into tests, not screenshots. The target is publish within five business days, reconcile logs and deployment events to one UTC timeline, separate trigger from contributing conditions, and assign every action an owner, due date, verification method, and risk-reduction claim. Write one assertion for the exact boundary, one immediately inside it, and one immediately outside it. Include denominator behavior when traffic is zero, missing, duplicated, delayed, or partially sampled.

The telemetry contract is container_oom_events_total, prompt_tokens, deployment_version, unavailable_requests_total, incident_action_overdue_total, and recurrence_test_passed. For every series, verify metric type, unit, label allowlist, ownership, and retention. Counters must only rise; histograms need buckets around the target; gauges require staleness handling. Confirm dashboards display demand, successful or protected outcomes, user latency or age, saturation, and version context together. A green ratio without its event count is not sufficient evidence.

Run the test workflow:

pytest -q tests/test_token_budget_regression.py
./ops/postmortem validate INC-274 --schema ops/postmortem.schema.json

Store machine-readable output as a build artifact. Seed deterministic time or fixtures where possible, but keep at least one integration test against the actual collector and rule engine. Tests must fail when the threshold expression, label name, or alert receiver is intentionally broken. A test that remains green after its signal is disconnected is worse than no test because it creates false confidence.

Use the configured contract:

postmortem:
  incident_id: INC-274
  impact_window_utc: [14:03, 14:20]
  sections: [impact, detection, timeline, causal_analysis, response, actions]
  actions_require: [owner, due_date, verifier, evidence]
  prohibited: [blame, counterfactual_certainty, unattributed_quotes]
review_due_business_days: 5

Replay the controlled scenario—analyze a supplied incident where a tokenizer upgrade doubled prompt size, exhausted worker memory, and caused 17 minutes of partial outage; reject hindsight claims unsupported by timestamped evidence.—and measure time from fault start to metric visibility, alert pending, alert firing, delivery, acknowledgement where relevant, mitigation, and stable recovery. Compare those times with the operational objective. Verify short benign blips do not page if the policy says they should not, while sustained or fast-burning impact does.

Check telemetry loss separately from service loss. Stop or misconfigure one scrape target and prove the system reports missing data rather than silently treating it as healthy. Verify logs and drill artifacts redact secrets and private payloads. Bound label cardinality using a representative workload and set an alert or budget for unexpected series growth.

Use the prior incident—tokenizer v4 counted whitespace differently, doubled p95 prompt tokens, OOM-killed workers, and returned 503 for 23% of summary requests from 14:03 to 14:20 UTC.—to ask which test would have detected the problem earlier. Remove or demote signals that do not lead to a decision. The production tradeoff is deep analysis finds systemic conditions but delays publication; many corrective actions feel thorough but dilute ownership and hide which controls materially reduce risk. Quantify the cost of collection and paging against the risk reduction it buys.

This page passes only when contract tests, failure tests, alert-path tests, and recovery assertions all run from a clean environment and produce retained evidence linked to the configuration revision.