Performance · Experiment

Performance marketing experiment AI agent

Coordinate tests across paid, landing, and outbound so you learn once — not five conflicting times.

One experiment system for growth

Channel teams accidentally collide: Meta tests a price while SEO changes the LP. The experiment agent owns a shared calendar, success metrics, and cleanup so learnings stick.

Observe → decide → act

What this agent watches, how it decides, and what it executes inside your guardrails.

Watches
  • Active tests across channels and landing pages
  • Sample size and interference risks
  • Primary and guardrail metrics (CVR, CAC, reply rate)
  • Past experiment library to avoid retests
Decides
  • Which portfolio test is highest learning value
  • How to isolate traffic to avoid interference
  • Ship / iterate / kill with a written conclusion
Acts
  • Registers experiments and freeze windows
  • Coordinates specialist agents for each arm
  • Publishes results into the growth playbook

Example actions

  • Run a pricing LP test with paid traffic only; freeze SEO edits for 14 days

  • Test outbound offer A/B while holding LinkedIn ads creative constant

  • Conclude a channel mix test and update budget policy

Integrations

  • Ad platforms
  • CMS / LPs
  • Sequencers
  • Analytics
  • Slack

FAQ