Design retries, degradation, and recovery

Map failure classes for POST /answer: retryable vs fatal vs needs-human. Retries need budgets, jitter, and idempotency rules aligned to promote only if canary error_rate ≤ baseline+0.5pp and groundedness ≥ baseline−1pp for two 10m windows. The chapter’s signature failure — automation promotes while scrape_up{revision=v2}==0 — must take a deliberate branch, not a generic catch-all.

Implement the degrade/rollback/refuse behavior release commander watching revision-sliced error rate needs when CANARY-PROMOTE-BLIND-6 repeats. Prefer scoped controls (one flag, one weight, one tenant, one secret version) over fleet-wide restarts. Preserve evidence; do not delete logs to “clean the demo.”

After containment, check canary_weight, error_rate_by_revision, grounded_rate_by_revision moves in the safe direction and watch for retry amplification. Write the stop condition that ends the incident response for this lab.

Chaos note: inject only one fault class at a time and restore fixtures after. Watch for dual failures — dependency down and retry amplifier — which is how automation promotes while scrape_up{revision=v2}==0 becomes an outage. Customer communication templates (even if only for the drill) beat silence. If you queue deferred work, define poison-message handling. Budget documents should state the maximum extra spend allowed during retries. Close the loop by linking the containment action to a dashboard panel for canary_weight, error_rate_by_revision, grounded_rate_by_revision.

Draw a state diagram for degrade modes and put it in the repo as ASCII if needed. Cap concurrent retries across the process, not only per request. Ensure cancellation propagates to downstream HTTP clients. When failing closed, choose a user-visible message that does not leak internals. Practice the single command that flips the kill switch or weight to zero. After recovery, drain or inspect deferred work before declaring green. In this chapter the product is containerized answer API releasing image v2 at 5%→25%→100% weights, the human stakeholder is release commander watching revision-sliced error rate, and the incident id you design against is CANARY-PROMOTE-BLIND-6. Re-state the oracle in your notes — 5% canary healthy 20m → promote to 25%; guardrail breach → weight 0 in < 2m — and keep the invariant visible: promote only if canary error_rate ≤ baseline+0.5pp and groundedness ≥ baseline−1pp for two 10m windows. Track canary_weight, error_rate_by_revision, grounded_rate_by_revision as the scoreboard. Surface under change control: POST /answer. If you only have forty minutes, finish the fixture for automation promotes while scrape_up{revision=v2}==0 before polishing UI. Promotion language stays ternary: promote, hold, or roll back based on evidence, not hope.