Train and evaluate agents against real commerce.
We are developing environments grounded in authorized commercial state and observable outcomes, so teams can evaluate how agents make buying decisions.
What is available?
Products, prices, inventory, policies and permissions.
What can the agent do?
Compare options and choose supported commercial actions.
What actually happened?
Completion, failure and observable commercial results.
Commerce changes while an agent is deciding.
Changing state
Prices, availability and delivery conditions change. An agent needs to reason against the relevant state.
Business constraints
Policies, authorization and supported actions determine what an agent can actually do.
Meaningful outcomes
A plausible answer and a completed commerce task are different observations. Evaluation needs a defined result.
From merchant context to useful evaluation.
Merchant context & outcomes
Agentic Page provides the starting point in merchant content, AI discovery and available attribution.
Structured trajectories
Connect authorized state, supported actions and observable results into defined records.
Evaluation & training environments
Use these records to calibrate tasks and evaluate agent behavior under commercial constraints.
Help define the tasks worth measuring.
We welcome discussions about evaluation tasks, environment requirements and authorized data partnerships. Availability and collaboration scope are agreed individually.