Open Agents Need Their Training Trails

The July 8 article frames agent reliability as a data-transparency problem. Tool failures, recovery behavior, multi-step workflows, retrieval, safety decisions, and simulated users all shape an agent, yet those traces are often less visible than the final weights. The authors point to NVIDIA's Nemotron data releases and synthetic-data work as examples of a broader stack that can be inspected. This is an advocacy and ecosystem article rather than a new benchmark, but it identifies a concrete gap: open weights alone do not explain why an agent takes an action or how that behavior was trained.