AI AGENTS
A/B Experiment Iterate Verdict Spawns Linear Follow-up
When an experiment platform webhook signals a finished test, an agent reads the results; if the verdict is iterate, it auto-creates a scoped Linear issue proposing the next…
How it runs
The automated pipeline, trigger to output.
- TriggerExperiment-complete webhook receivedHTTP webhook
- ActionFetch experiment results from BigQueryBigQuery
- LogicBranch: continue only if verdict is iterate
- ActionDraft next-variant hypothesis
- OutputCreate assigned follow-up issue in LinearLinear
What it does
Closes the loop on inconclusive experiments. When a test finishes, the agent evaluates the data, and specifically for iterate outcomes it writes a follow-up Linear issue describing why the result was inconclusive and what concrete next variant to test — so iteration never stalls in a backlog gap.
When to use it
Use this when your biggest experimentation leak is the iterate cases that quietly die. It guarantees every "needs another round" verdict becomes a tracked, assigned piece of work.
How it works
- 1A webhook fires when the experiment tool marks a test complete.
- 2A BigQuery action fetches the full results for that experiment.
- 3A logic branch routes only iterate verdicts forward; ship and kill exit early.
- 4The agent drafts the next-experiment hypothesis and proposed variant change.
- 5A Linear action creates the issue, scoped and assigned to the experiment owner with the rationale attached.
Set it up
What you configure once, before turning it on.
- 1Connect HTTP webhookTrigger any URL on agent actions.
- 2Connect BigQueryDatasets, queries, schemas.
- 3Connect LinearIssues, projects, cycles, triage.
- 4Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
- 5Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
- 6Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.
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