As AI agents handle more complex tasks, their decision trails grow too vast to review manually. Researchers have developed a lightweight system that acts like an intelligent filter, instantly spotting which agent interactions are worth keeping without slowing down the system or costing a fortune. This new method calculates simple signals from live data to separate high-value learning moments from noise, solving the bottleneck of post-deployment improvement. By organizing these signals into categories like interaction issues, execution failures, and environment limits, the framework identifies informative paths with 82% informativeness rate. This outperforms standard filtering techniques that only catch obvious errors or random sampling by a significant margin. The result is a practical infrastructure that boosts efficiency while ensuring systems learn from the most critical experiences rather than wasting resources on repetitive loops. Source: Signals: Trajectory Sampling and Triage for Agentic Interactions by Shuguang Chen, Adil Hafeez, Salman Paracha, https://arxiv.org/abs/2604.00356