Define the production target for on-call operations

This lab builds humane escalation path for production AI answer platform. The human in the loop is primary engineer receiving SEV2 after hours. Scope is intentionally narrower than “make AI reliable”: you will prove one oracle — game day: primary misses ACK → secondary owns INC-OC-554 in 15m with access grant — and one invariant — missed ACK in 15m escalates; SEV1 pages dual; handoff includes timeline + next action. Record non-goals in your notes so a later change cannot silently expand authority. The incident mnemonic for the chapter is INC-OC-554; design as if that ticket is already written and you are filling evidence.

Turn the oracle into a table: input fixture, expected observable, prohibited side effect, owner, latency/cost ceiling. Separate model taste from software correctness — transport, auth, parsing, and termination must be deterministic even when generated text varies. Primary metric family: ack_minutes and pages_per_week. Averages without a denominator or revision label do not gate release. Fake external dependencies in unit tests; live calls wait until fakes pass.

Before implementation, encode a failing check that would have caught everyone paged for SEV3 noise → real SEV1 ignored. That failure is the pedagogical north star for later pages: contracts reject it, happy path never performs it, validation asserts it, failure-handling contains it, observability detects it, security-ops prevents privilege tricks around it, and mastery replays it in a drill. Endpoint under study: PagerDuty schedule ai-answer-primary.

Capacity note for planners: estimate peak demand on PagerDuty schedule ai-answer-primary and the cost ceiling for a failed retry storm. Write the abort conditions — unbounded spend, cross-tenant leakage, or inability to roll back — before you enjoy the first green test. Prefer synthetic fixtures shaped like production over anonymized production dumps you cannot share in class. When you are tempted to widen scope, re-read the oracle (game day: primary misses ACK → secondary owns INC-OC-554 in 15m with access grant) and cut features that do not serve it. The teaching outcome is judgment under constraints: primary engineer receiving SEV2 after hours gets a trustworthy control, not a kitchen-sink framework. Keep the language of release decisions: promote, hold, or roll back — never “see if it gets better.”

Decide what will live in version control on day one: fixtures, contract markdown, and a failing test name. Write the cost ceiling as a hard number with currency and period. If the lab involves clusters, name the non-prod context you will use and forbid prod kubecontexts in scripts. Capture the baseline metric once before changing code so later gains are comparative. Refuse tools that hide the request path behind magic macros until the oracle is green on fakes. Your README section for this page should be five lines or fewer and still falsifiable. In this chapter the product is humane escalation path for production AI answer platform, the human stakeholder is primary engineer receiving SEV2 after hours, and the incident id you design against is INC-OC-554. Re-state the oracle in your notes — game day: primary misses ACK → secondary owns INC-OC-554 in 15m with access grant — and keep the invariant visible: missed ACK in 15m escalates; SEV1 pages dual; handoff includes timeline + next action. Track ack_minutes and pages_per_week as the scoreboard. Surface under change control: PagerDuty schedule ai-answer-primary. If you only have forty minutes, finish the fixture for everyone paged for SEV3 noise → real SEV1 ignored before polishing UI. Promotion language stays ternary: promote, hold, or roll back based on evidence, not hope.