New framework uses lightweight signals to efficiently filter and prioritize AI agent trajectories for improvement without slowing down live systems

Researchers propose a signal-based triage system to identify valuable agent interaction logs from LLM-based applications without manual review overhead

The approach computes cheap, reusable signals from live interactions and categorizes them into three types: interaction (misalignment, stagnation, disengagement, satisfaction), execution (failure, loop), and environment (exhaustion)

Solution addresses the challenge of managing voluminous and non-deterministic agent trajectories that would be too costly to review manually or with auxiliary LLMs at scale