An auto-research agent is like a 24/7 experiment-running intern

The use case: Create a system that repeatedly proposes an experiment, implements it, runs a small test, evaluates the result, and keeps only the winners—then repeats.

This is especially powerful for business functions where iteration is cheap and measurable: landing pages, onboarding flows, pricing pages, email subject lines, ad creatives, and even support macros. Instead of running one A/B test per week, the organization can run dozens of micro-tests and surface the best-performing variants for humans to approve and roll out broadly.

How to use this in a real company:

• Define the metric: conversion rate, booked demos, trial-to-paid, churn, average order value, etc.
• Constrain the sandbox: only edit specific components (headline, CTA, hero copy) to reduce risk.
• Automate evaluation: require statistical thresholds and guardrail metrics (bounce rate, refund rate).
• Human approval loop: let the agent propose winners; humans approve deployment to production.

Most teams already have the ingredients—analytics, feature flags, and copywriting. The difference is connecting them into a repeatable ‘experiment factory’ that runs continuously.