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Foundations of Proactive Agents: Principles, Technical Layers, and Proactivity-Gym

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Briefings

This article proposes a framework for proactive LLM agents using three joint principles: Task Capability, Temporal Allocation, and Trust. It introduces PROACTIVITY-GYM, a simulation testbed evaluating agents across five design dimensions. Research on 23 model-harness configurations revealed performance gaps where LLM judges conflate task capability with trust. A human study with 30 participants showed trust declines after intervention misalignment, even with correct outcomes, and users prefer sleep-time assistance over imperfect real-time help.

Key points

• The framework defines three joint principles (3T): Task Capability, Temporal Allocation, and Trust to guide proactive LLM agent design.
• PROACTIVITY-GYM is a simulation-based testbed evaluating agents across five dimensions including task scope, anticipation horizon, and activation trigger.

Context

Proactive LLM agents aim to turn idle compute into useful support before users ask, but current systems risk misreading context, imposing review costs, or undermining trust. This work addresses these challenges through the 3T framework and PROACTIVITY-GYM.

Limitations

The study relies on 23 model-harness configurations and a single human study with 30 participants. Findings are observational and do not account for all environmental or user-specific variables.

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