How it compares
How it compares
How is this different from A/B testing?
A/B tests use fixed traffic splits over a defined period. The agent adjusts traffic in real time, personalises by visitor group, and runs continuously — so performance improves over time rather than simply producing a report at the end.
Does this replace A/B testing entirely?
No. A/B testing is still the right tool when you want a one-off, unambiguous answer, e.g. "should the hero CTA say 'Shop now' or 'Buy now'?"
Agentic Campaigns are the right tool when the answer is different for different audiences and you want the site to keep responding correctly as those audiences evolve. Many MWI clients run both: A/B tests to validate specific design hypotheses, Agentic Campaigns to operate those decisions at scale across audiences.
How is this different from Custom Campaigns?
With Custom Campaigns, you define who to target, when to show the experience, and what the journey looks like. With Agentic Campaigns, you provide the experiences and the strategic intent — the agent finds the right visitors and the right moments automatically.
Control & Safety
Control & Safety
Do I have control once it’s live?
Yes. You retain control of targeting rules and can pause or turn off the campaign at any time. The agent manages optimisation decisions within the boundaries you set.
You can also:
Include a control or baseline experience as a fallback and comparison point
Adjust Control Share at any time, or use Optimised Control to have the agent manage it automatically as confidence grows
Set Optimisation Speed to control how quickly the agent commits to a winner
Update your custom prompt to adjust how the agent reasons about your business context
Guide segmentation by choosing the strategy that best fits your audience and goal
How does the agent play it safe?
The agent has several mechanisms for protecting performance while it learns.
Guardrail metrics let you monitor transactional performance continuously, even when optimising for a different goal. If revenue or conversion drops beyond a set threshold, you receive a Warning or Alert in the report before the impact compounds.
You can also include a control or baseline experience alongside the standard control. If the agent is not confident in any of your variations, it will route traffic toward the safest option. Optimised Control manages control group size automatically, keeping it higher while uncertainty is high and reducing it as confidence builds. You can turn off the campaign at any time.
Measuring Performance & Transparency
Measuring Performance & Transparency
How do I know it’s working?
Check for the green “Data Received” checkmark on the Campaigns page — then head to the Agentic Report for a full view of how it’s performing, where traffic is going, and why.
Is this a black box?
Not fully. The agent combines statistical methods, contextual modelling, and reasoning AI to make decisions — and provides clear reporting signals and summaries to explain what’s happening and why.
The Agent Logs in the Agentic Report give detailed insight into every decision made. Agent Summaries translate that into plain language for stakeholder-friendly communication.
Why does my report show optimised performance instead of the actual numbers?
The report defaults to the optimised view, which recalculates performance by applying the agent's final traffic allocations back to day one. This removes the distorting effect of the exploration phase, during which the agent is spreading traffic across experiences to learn, which suppresses the raw observed figures.
The optimised view gives a more accurate picture of the campaign's real impact. You can toggle to the observed view at any point using the view selector in the report.
How many segments does the agent actually create?
Up to 500 segment combinations per campaign (a cardinality cap we enforce so each segment retains enough sample depth to be statistically useful). On a typical retail site that resolves to ~60–120 active segments during Optimisation phase, spanning device × channel × intent × session-depth contexts.
Seasonality & Peak Trading
Seasonality & Peak Trading
How does it handle peak trading?
The default training window is a rolling 90 days, which helps the agent learn across a range of trading conditions. Higher traffic volumes during peak periods can actually help the agent adapt and learn faster. It continuously updates based on the most recent data.
Operational
Operational
How much bandwidth does this actually take?
Setup: 30–60 minutes per campaign (strategy choice, experience build, rules)
Ongoing: ~5 minutes a week to check the Performance Snapshot, Probability to Beat Control, and Biggest Movers
Monthly: 15–30 minutes to review the AI Decision Log and consider whether to add/remove experiences
Most MWI clients run 3–8 Agentic Campaigns concurrently with the bandwidth of a single CRO or marketing operations person.
What's the most effective way to run agentic campaigns?
For strategies that don't optimise timing, always have at least 2 experiences - as the Agent needs this in order to compare results.
It also works best if you have structurally different experiences. For example, if you were just testing the placement of the same experiences, there may not be enough contrast for the agent to find a winner.
Troubleshooting
Troubleshooting
What if it’s not working as expected?
First, check:
Campaign status is Live
Your experiences are live and correct on your site
Global rules are targeting the right audience
If something still looks off, check the Agent Logs in the Agentic Report for errors or unexpected behaviour. Contact support if you see repeated issues.
Allocations have shifted suddenly — is something wrong?
Usually no. Large day-on-day allocation shifts are expected during Exploration phase and after any of these events:
An experience was added or edited
A major traffic-composition change (paid campaign started, referral source spiked)
A seasonal demand shift (e.g. gifting season kicked in)
Check the Agent Activity tab for that day's Decision Log entry — it'll name the factor the agent responded to. If the log points to a data-quality issue (e.g. "goal events delayed") that's when to contact support.