Define the production target for postmortems

This lab builds blameless postmortem for tokenizer-induced summarization outage. The human in the loop is EM facilitating review of INC-SUM-TOK-87. Scope is intentionally narrower than “make AI reliable”: you will prove one oracle — postmortem published with 3 actions; action A1 verified by test that fails on old tokenizer pin — and one invariant — actions are owned, dated, verifiable; contributing factors include process not only code. Record non-goals in your notes so a later change cannot silently expand authority. The incident mnemonic for the chapter is INC-SUM-TOK-87; 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: actions_closed_on_time and recurrence_90d. 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 action 'be more careful' with no owner — recurrence in 6 weeks. 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: summarization service.

Capacity note for planners: estimate peak demand on summarization service 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 (postmortem published with 3 actions; action A1 verified by test that fails on old tokenizer pin) and cut features that do not serve it. The teaching outcome is judgment under constraints: EM facilitating review of INC-SUM-TOK-87 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 blameless postmortem for tokenizer-induced summarization outage, the human stakeholder is EM facilitating review of INC-SUM-TOK-87, and the incident id you design against is INC-SUM-TOK-87. Re-state the oracle in your notes — postmortem published with 3 actions; action A1 verified by test that fails on old tokenizer pin — and keep the invariant visible: actions are owned, dated, verifiable; contributing factors include process not only code. Track actions_closed_on_time and recurrence_90d as the scoreboard. Surface under change control: summarization service.