Large Language Model (LLM) agents represent a promising shift in human-AI interaction, moving beyond passive prompt-response systems to autonomous agents capable of reasoning, planning, and goal-directed action. While LLM agents are technically capable of performing a broad range of tasks, not all of these capabilities translate into meaningful usability. This position paper argues that the central question for LLM agent usability is no longer whether a task can be automated, but whether it delivers sufficient Agentic Return on Investment (Agentic ROI). Agentic ROI reframes evaluation from raw performance to a holistic, utility-driven perspective, guiding when, where, and for whom LLM agents should be deployed. Despite widespread application in high-ROI tasks like coding and scientific research, we identify a critical usability gap in mass-market, everyday applications. To address this, we propose a zigzag developmental trajectory: first scaling up to improve information gain and time savings, then scaling down to reduce cost. We present a strategic roadmap across these phases to make LLM agents truly usable, accessible, and scalable in real-world applications.

翻译：大型语言模型（LLM）智能体代表了人机交互领域一个前景广阔的转变，它超越了被动的提示-响应系统，发展成为能够进行推理、规划和目标导向行动的自主智能体。尽管LLM智能体在技术上能够执行广泛的任务，但并非所有这些能力都能转化为有意义的实用性。本立场论文认为，LLM智能体实用性的核心问题已不再是任务能否被自动化，而在于其是否能提供足够的智能体投资回报率（Agentic ROI）。智能体投资回报率将评估框架从原始性能转向整体的、效用驱动的视角，从而指导LLM智能体应在何时、何地以及为谁部署。尽管LLM智能体在编码和科学研究等高投资回报率任务中得到了广泛应用，但我们发现其在面向大众市场的日常应用中存在关键的实用性差距。为解决这一问题，我们提出一种之字形发展路径：首先通过扩大规模以提高信息增益和节省时间，随后通过缩小规模以降低成本。我们提出了跨越这些阶段的战略路线图，旨在使LLM智能体在现实应用中真正实现可用性、可访问性和可扩展性。