Signals: Trajectory Sampling and Triage for Agentic Interactions
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 interaction trajectories. Our approach computes cheap, broadly applicable signals from live interactions and attaches them as structured attributes for trajectory triage, identifying interactions likely t...
| Authors | Shuguang Chen, Adil Hafeez, Salman Paracha |
|---|---|
| Primary category | cs.AI |
| Published | 1 April 2026 |
| Last updated at source | 1 April 2026 |
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