A lightweight framework for triaging agentic trajectories post-deployment without the cost of human review or auxiliary LLM calls.
April 2, 2026
Original Paper
Signals: Trajectory Sampling and Triage for Agentic Interactions
arXiv · 2604.00356
The Takeaway
By using 'signals' (misalignment, stagnation, etc.) computed from live interactions, practitioners can identify informative trajectories for improvement 1.5x more efficiently than heuristic filtering, significantly reducing the data-labeling bottleneck for agents.
From the abstract
Agentic applications based on large language models increasingly rely on multi-step interaction loops involving planning, action execution, and environment feedback. While such systems are now deployed at scale, improving them post-deployment remains challenging. Agent trajectories are voluminous and non-deterministic, and reviewing each one, whether through human review or auxiliary LLMs, is slow and cost-prohibitive. We propose a lightweight, signal-based framework for triaging agentic interac