Observability for AI agents goes beyond simply collecting logs and traces. According to Machine Learning Mastery, the spans generated by an agent's execution don't mean much as a raw list. Without structure, developers are left with a flat sequence of events that obscures how each step relates to the others.
The solution is chain visualization, often rendered as a trace waterfall. This view arranges spans hierarchically, showing which operations triggered which sub-operations and how long each took. By reading the waterfall, developers can quickly spot bottlenecks, failed branches, or unexpected loops in an agent's reasoning.
The source emphasizes that this visual layer is not a luxury but a practical necessity. For AI agents, where behavior emerges from many interconnected calls, a readable trace is what turns raw telemetry into actionable insight.