AI AGENTS

Replicate Golden-Prompt Regression Snapshot to Notion

On a weekly cadence the agent re-runs your golden prompts against every pinned Replicate version, computes drift versus last week's baseline.

CategoryAI Agents
Enginesim
Difficultyintermediate
Triggerschedule
Steps6
Setup~15 min

How it runs

The automated pipeline, trigger to output.

  • TriggerWeekly schedule starts the regression run
  • ActionRun golden prompts against each pinned Replicate versionReplicateReplicate
  • ActionLoad prior baseline outputs from PostgresPostgreSQLPostgres
  • LogicDiff against baseline and flag prompts over threshold
  • ActionPersist fresh outputs as next baseline in PostgresPostgreSQLPostgres
  • OutputPublish regression snapshot to NotionNotionNotion

What it does

Even without a formal deprecation, Replicate-hosted models can shift behavior. This agent runs your golden prompt set weekly against each pinned version, compares the outputs to the prior week's stored baseline, computes per-prompt drift, and publishes a readable regression snapshot to Notion so the team can spot creeping quality changes early.

When to use it

Use it for ongoing model-health monitoring when you want a standing record of how stable each inference endpoint is over time, independent of any deprecation event.

How it works

  1. 1A weekly schedule kicks off the regression run.
  2. 2The agent runs every golden prompt against each pinned Replicate version.
  3. 3It loads last week's baseline outputs from Postgres and diffs them against the fresh results.
  4. 4A logic step flags prompts whose drift crosses the alert threshold.
  5. 5It writes the new outputs back to Postgres as next week's baseline.
  6. 6It publishes a Notion snapshot with the drift table and flagged prompts highlighted.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect ReplicateImage, video, and model inference.
  2. 2
    Connect PostgresAny Postgres URL — query, write, migrate.
  3. 3
    Connect NotionPages, databases, comments.
  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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