Make dimensions, materials, variants and room constraints understandable enough for an agent to choose the right configuration.
Test a furniture journey →Help AI agents
choose the right product.
AgentCart tests whether an AI shopping agent can understand, compare, configure and move the right product toward purchase — then shows what blocks it.
Find the right product for this customer, resolve the important constraints, and move the best match toward purchase.
AgentCart identifies the first point where the product decision becomes unreliable.
≠ human-readable
at a time
Your store can look perfect to a person.
An agent can still fail.
AgentCart does not ask whether your storefront “looks AI-ready.” It executes a purchase journey and exposes where machine action breaks.
One product decision is unclear to the agent.
The agent reaches the right product family, but a key constraint is ambiguous enough to make the next purchase step unreliable.
The prototype check now passes for the previously ambiguous purchase constraint. No sales-lift claim is implied.
One product. Three niche-specific outcomes.
Make goals, ingredients, shades, routines and product families clear enough for an agent to recommend a relevant match.
Test a beauty journey →Make specs, generations, accessories and compatibility explicit enough for an agent to avoid the wrong product or add-on.
Test an electronics journey →Find the block.
Fix the block.
Run it again.
Connect
Point AgentCart at the storefront.
Run
Execute an agent purchase journey.
Fix
Surface one merchant-controllable action.
Verify
Run the journey again and compare.
Less dashboard.
More decision.
Catalog diagnostics, journey traces and evidence stay behind the scenes. The merchant sees what matters now: the biggest blocker, the action, and the verification.
Open AgentCart →