Researchers built Atlas, a visual system for making sense of agent runs that span many steps. The core problem: after a long run finishes, a human has no practical way to review what happened. A human also cannot easily see why the agent made the choices it did, or where things went wrong. Atlas addresses this with a structured visual framework. It groups agent actions into meaningful segments, surfaces decision points, and links outcomes back to the original goal. The system was validated through a user study, giving it an empirical basis that most proposed solutions in this space lack. The study examined how humans reconstructed understanding of a completed run. It looked at what information they needed first, and where existing representations failed them. The findings suggest that flat logs and step-by-step replays are insufficient. They put the burden of synthesis on the human rather than on the representation itself. For teams building agent-powered products, Atlas surfaces a design tension worth attention. The interface problem does not end when the agent finishes. A human reviewing finished or blocked work needs a summary structure that preserves causality and flags consequential decisions. Atlas is a research prototype, but its framework and study results give product teams a concrete starting point for designing the post-run review screen.