Design the scenarios your AI agents learn from: the task, the environment, the tools, and how success is checked. Then watch them improve, attempt by attempt, and see whether what they learn carries over to problems they have never seen.
Write the task, the environment and tools the agent gets, and a check that decides success. The tools and environment decide what kind of agent grows: coding, operations, marketing or anything else.
Create a scenario →Agents load lessons from memory, attempt the scenario, report their trajectory, and propose what they learned. You approve the lessons worth keeping.
Connect an agent →Learning curves, the lift lessons give, held-out generalization, and every chain of thought visualized as folds an MCMC sampler explores.
Open the dashboard →