Implement the happy path

Code the narrow path that serves SRE watching error budget during a model bump: accept → authorize/normalize → call dependency → validate → record. Keep stages named so a trace can show which boundary passed. Success must emit evidence useful to alert_precision on game-day ≥ 0.9 and scrape_up == 1, not only a 200 with prose. Predict the observable for GET /metrics before running: inject 5% 500s for 10m → Alert AnswerErrorBurn fires; recovery clears within 15m.

Drive the path with recording fakes or local stubs. Assert call order and arguments. Idempotency keys or stable ids should keep retries from duplicating costly work where the product requires it. Product under test remains Prometheus+Grafana board for AI answer API golden signals — resist adding unrelated features mid-path.

For Production monitoring lab, paste the CLI/HTTP transcript beside your prediction for GET /metrics. If the oracle is unmet (inject 5% 500s for 10m → Alert AnswerErrorBurn fires; recovery clears within 15m), stop and debug this page; do not compensate with prompt folktales. Re-run once after a clean process start to catch hidden global state that would invalidate MON-TENANT-BLIND-9.

Performance sketch: measure local p95 for the fake-backed path so later regressions are obvious. Keep concurrency modest until failure-handling proves limits. Log a single structured event per success with request id, revision, and the evidence field behind alert_precision on game-day ≥ 0.9 and scrape_up == 1. Avoid hidden global caches in the happy path unless the lab is about caching — and even then key by tenant. If the path calls a model, pin model id in config and echo it in the response for auditability. Remember SRE watching error budget during a model bump experiences wall-clock time, not your debugger’s single-step comfort.

Prefer explicit function names over a single god-object handleRequest. Thread a correlation id from ingress to the last log line. When streaming, define what partial failure means before coding. Snapshot one successful response body in fixtures after redaction. If the path writes to a queue, assert message attributes in the fake. Stop adding retries on this page; that is the next concern. In this chapter the product is Prometheus+Grafana board for AI answer API golden signals, the human stakeholder is SRE watching error budget during a model bump, and the incident id you design against is MON-TENANT-BLIND-9. Re-state the oracle in your notes — inject 5% 500s for 10m → Alert AnswerErrorBurn fires; recovery clears within 15m — and keep the invariant visible: alerts require multi-window evidence; missing scrape ≠ healthy silence. Track alert_precision on game-day ≥ 0.9 and scrape_up == 1 as the scoreboard. Surface under change control: GET /metrics. If you only have forty minutes, finish the fixture for dashboard averages hide tenant Acme 40% error rate before polishing UI. Promotion language stays ternary: promote, hold, or roll back based on evidence, not hope.