AI AGENTS
A/B Result Row Triggers Confluence Decision Memo
When a results row lands in your Postgres experiment table, an agent reads the variant data, decides ship/kill/iterate, and publishes a formatted decision memo to a Confluence…
How it runs
The automated pipeline, trigger to output.
- TriggerNew results row inserted in PostgresPostgres
- LogicConfirm row is final, not partial
- LogicDecide ship / kill / iterate
- ActionDraft formatted decision memo
- OutputPublish memo page to ConfluenceConfluence
What it does
Watches your experiment results table and acts the moment final numbers are written. The agent reads the new row, reaches a verdict, and publishes a clean decision memo to Confluence — keeping the official record where engineering and product already document specs and outcomes.
When to use it
Use this when your experiment results pipeline writes to Postgres and your team's source of truth lives in Confluence. It removes the lag between "data is final" and "decision is documented."
How it works
- 1A trigger fires when a new final-results row is inserted into the Postgres experiment table.
- 2A logic step confirms the row is marked complete and not a partial flush.
- 3The agent evaluates significance and lift to decide ship, kill, or iterate.
- 4The agent drafts a formatted memo with the hypothesis, metrics, and verdict.
- 5A Confluence action publishes the memo page under the experiments space.
Set it up
What you configure once, before turning it on.
- 1Connect PostgresAny Postgres URL — query, write, migrate.
- 2Connect ConfluenceSpaces, pages, blueprints.
- 3Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
- 4Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
- 5Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.
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Run this workflow in your colony.
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