Plan-then-execute: An empirical study of user trust and team performance when using llm agents as a daily assistant

Analysis

Why This Paper Matters

As large language model (LLM) agents become increasingly integrated into daily workflows, understanding their impact on human trust and team dynamics is critical. This paper addresses a timely question: how do LLM agents, when acting as daily assistants, influence user trust and team performance? The authors frame their investigation around a 'plan-then-execute' paradigm, where agents generate plans and then carry them out. This is significant because trust is a cornerstone of effective human-AI collaboration, and missteps can erode confidence and hinder productivity.

The study's empirical approach provides much-needed evidence on the real-world consequences of deploying LLM agents. While much research focuses on technical capabilities, this work shifts attention to human factors, offering insights that can inform design principles and deployment strategies. By highlighting both positive and negative outcomes, the paper underscores the complexity of integrating AI into collaborative environments.

Technical Contributions

• Empirical Framework: The paper introduces a structured study to evaluate user trust and team performance when LLM agents plan and execute tasks.
• Double-Edged Sword Finding: It identifies that agents can be beneficial but also harmful, particularly when their plans appear plausible but lead to suboptimal or incorrect actions.
• Insights for Practice: The authors synthesize actionable insights for using LLM agents effectively, likely including guidelines for transparency, verification, and task allocation.
• Trust Dynamics: The study examines how trust evolves over time and how it relates to agent reliability and plan quality.

Results

The abstract reports that LLM agents can be a 'double-edged sword,' indicating mixed effects on trust and performance. While specific metrics are not provided in the abstract, the finding that agents with plausible plans can mislead users suggests that trust may be misplaced when plans are superficially convincing. This implies that team performance could suffer if users over-rely on agent outputs without adequate verification. The study likely presents quantitative and qualitative results showing conditions under which agents help or hinder.

Significance

This work has broad implications for the design of LLM-based assistants. It calls for mechanisms that help users calibrate trust appropriately, such as explanations, confidence scores, or verification steps. For teams, it highlights the need for clear roles and oversight when integrating AI agents. As LLM agents become more autonomous, understanding their social and cognitive impact is essential for responsible AI development. The paper contributes to the growing field of human-AI interaction and provides a foundation for future research on optimizing collaboration between humans and LLM agents.