ENGINEERING

Nightly Replicate inference drift watch with auto-rollback

Each night replays a canary prompt suite against the live Replicate endpoint, detects output drift or latency regression versus a stored baseline.

CategoryEngineering
Enginesim
Difficultyintermediate
Triggerschedule
Steps6
Setup~15 min

How it runs

The automated pipeline, trigger to output.

  • TriggerNightly schedule
  • ActionReplay canary suite on live versionReplicateReplicate
  • LogicDetect drift / latency regression vs baseline
  • ActionRoll endpoint alias back to last-known-goodReplicateReplicate
  • ActionOpen incident with failing casesPagerDutyPagerDuty
  • OutputPost drift summary to SlackSlack

What it does

Guards a production Replicate endpoint against silent drift over time. Every night it replays a fixed canary suite, scores the outputs against a stored baseline, and if quality or latency has degraded past tolerance it automatically rolls the endpoint alias back to the last-known-good version and escalates.

When to use it

Use it for endpoints that must stay stable between releases, where upstream model changes, autoscaling cold starts, or config drift could quietly degrade results. It catches regressions that no PR introduced.

How it works

  1. 1A nightly schedule trigger starts the run.
  2. 2The flow replays the canary prompt suite against the current production Replicate version and records scores and p95 latency.
  3. 3A logic step diffs the run against the stored baseline to flag accuracy drift or latency regression.
  4. 4If a regression is detected, it repoints the Replicate endpoint alias back to the last-known-good version.
  5. 5It opens a PagerDuty incident with the failing cases and the rollback action taken.
  6. 6It posts a summary with the drift chart link to Slack.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect ReplicateImage, video, and model inference.
  2. 2
    Connect PagerDutyIncidents, on-call, escalations.
  3. 3
    Connect SlackChannels, DMs, threads, mentions.
  4. 4
    Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
  5. 5
    Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
  6. 6
    Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.

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