Position: The Real Barrier to LLM Agent Usability is Agentic ROI

TL;DR

This paper argues that the key to improving LLM agent usability is focusing on Agentic ROI, emphasizing utility over raw performance, and proposes a developmental roadmap for real-world deployment.

Contribution

It introduces the concept of Agentic ROI as a new evaluation framework and outlines a strategic roadmap for scaling LLM agents for practical, everyday use.

Findings

Agentic ROI shifts focus from performance to utility.

High-ROI tasks like coding show promising results.

A phased developmental approach is proposed for broader usability.

Abstract

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.