experiment-validation

Structured experiment validation works by reading upstream Design and Define artifacts to extract hypotheses, creating prioritized L3-L4 experiment plans, enforcing guardrail criteria, and generating a validated Go/No-Go report. This ensures feature-level validation decisions are defensible and traceable.

Make Go/No-Go decisions by collecting experimental data from A/B tests, prototype tests, or bench tests, then running a multi-agent advocate/challenger/observer inquiry session. This enforces success and kill criteria to produce a validated report with traceable provenance.

Enforce guardrail and kill criteria during A/B experiment data collection by applying the experiment plan's statistical and guardrail parameters. The workflow tracks progress, collects data, and validates results against these pre-defined constraints before producing a final report.

Yes, you can use Feishu project MCP workitems for experiment design and tracking. The validation workflow optionally queries Feishu project MCP to reconcile upstream artifacts and support data collection for L3-L4 verification processes.

Yes, you need upstream Design and Define artifacts to start hypothesis testing. The validation process automatically reads and reconciles these artifacts to extract hypotheses, success criteria, constraints, and risks required for structured experiment planning.

Multi-agent inquiry for L3-L4 feature validation works by running advocate, challenger, and observer agents to assess collected experimental data. This session challenges assumptions against guardrail metrics to ensure the Go/No-Go decision is robust and defensible.