12-validation-agent — Journey Mapping + Experiment Design (Step 2b)

Journey mapping for confirmed hypotheses traces the approved funnel scope across frontend and backend layers to surface user flow, system guards, and next steps. This extracts implementation context to inform experiment design without exposing raw code.

Designing A/B tests from code analysis requires reading targeted implementation logic to generate deterministic experiment plans. This yields structured A/B test designs with confidence scoring, ready for handoff to PM and Engineering teams for execution.

Journey mapping within an intelligence loop operates after the PM gate confirms hypotheses, applying diagnostic workflows to map the approved scope. It generates structured outputs like experiment-designs.json and read-audit.log to feed downstream experiment execution.

Yes, experiment plans can be generated through targeted code reading that extracts implementation context without exposing raw code. The process outputs structured design files and confidence scores while maintaining code abstraction for PM consumption.

Experiment design handoff requires structured outputs including experiment-designs.json, experiment-designs.md, read-audit.log, and run-log. These files contain the deterministic test plans, confidence scoring, and journey context needed for Engineering execution.

Journey mapping should start after the PM gate confirms hypotheses and approves the diagnostic scope. Spawning the mapping agent at this intelligence loop gate ensures journey analysis targets confirmed problems before designing experiments.