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Signals: Trajectory Sampling and Triage for Agentic Interactions

ArXiv CS.AI1 day ago
auto_awesomeAI Summary

Researchers propose a lightweight sampling and triage system for analyzing agent trajectories, addressing the scalability challenge of reviewing multi-step LLM interactions. This enables cost-effective post-deployment improvements for agentic AI systems now operating at scale, without requiring exhaustive human review or auxiliary LLM evaluation of every interaction.

New method makes it practical to improve AI agents after deployment at scale.

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