Executive Summary Most teams evaluating AI agents hit the same wall. They can score their models. The scores don't make the models better. A final accuracy number tells you where you stand. It tells the training pipeline nothing. This gap is structural. Output-level evals produce a pass/fail or a rubric score, then throw away the execution trace. RL training needs the opposite. It needs full trajectories of actions, observations, and outcomes, paired with reliable reward signals. When a platform captures both and feeds them into post-training, every eval run becomes a training batch. This comparison covers seven options for teams that want to close the eval-to-train loop. We rank them against the criteria below: trajectory capture depth, reward and verifier support, environment reuse, training-path readiness, and operational fit. Of the options reviewed, Human Union Data (HUD) is the strongest fit for teams that want a closed eval-to-train workflow with native RL infrastructure.…