An Agentic Campaign is one that learns and improves on its own — without requiring manual intervention after launch. You add your experiences, choose a strategy, and an AI agent handles the rest: who sees what, when they see it, and how traffic is allocated across every audience segment.
Instead of running one A/B test at a time and locking in a fixed winner, the agent continuously gathers evidence, updates its understanding of what works best for each audience, and shifts traffic accordingly.
The two things the agent does
1. Decides who sees what (Contextual Allocation)
The agent decides which experience each visitor gets based on their full behavioural context: intent level, device, channel, landing page, product affinity, shopper mindset, and more.
The key difference from standard A/B testing: there's no single winner. Different experiences can win for different audiences. A social proof message that works brilliantly for first-time mobile visitors might underperform for returning desktop customers — and the agent learns this automatically.
Think of it as running hundreds of optimisations simultaneously, not one test with one winner.
2. Decides when to show it (One-Shot Optimisation)
For experiences that should fire once per visitor — email capture, discount overlays, exit-intent pop-ups — the agent doesn't just decide who sees it. It optimises when in their journey to show it.
The hypothesis is that the same intervention has very different effects depending on where a visitor is in their session.
How it learns
When a campaign first launches, the agent starts by spreading traffic fairly across all your experiences. This is the exploration phase — it’s gathering data, not yet making strong calls.
As patterns emerge, it shifts more traffic toward the better-performing experiences — while keeping some traffic on the alternatives to keep learning. It never fully commits. This is the exploitation phase, and it happens gradually as confidence builds.
The agent also uses shared learning: insights from one visitor group can inform decisions in another, which helps it optimise faster when data is still sparse.
All of this happens automatically. You don’t need to set thresholds, pick a winner, or make manual decisions.
The control variant
Every agentic campaign keeps a control group — visitors who see your default site experience, with no personalisation applied.
This is how the campaign measures uplift. The agent compares personalised visitors against the control to understand the real impact of the campaign.
We recommend also adding a 'Do Nothing' experience to your campaign. This allows traffic to shift to this variant if the experiences aren't performing effectively.