The Term 'Agent' Has Been Diluted Beyond Utility and Requires Redefinition

TL;DR

This paper argues that the term 'agent' in AI has become too vague and proposes a clear redefinition with a framework to improve communication, evaluation, and policy development in the field.

Contribution

It introduces a new framework with minimum criteria for defining 'agent' and characterizes systems along multiple dimensions to enhance clarity and consistency.

Findings

Proposes a multidimensional spectrum for system characterization

Provides a set of minimum requirements for systems to be considered agents

Offers recommendations for terminology standardization and framework adoption

Abstract

The term 'agent' in artificial intelligence has long carried multiple interpretations across different subfields. Recent developments in AI capabilities, particularly in large language model systems, have amplified this ambiguity, creating significant challenges in research communication, system evaluation and reproducibility, and policy development. This paper argues that the term 'agent' requires redefinition. Drawing from historical analysis and contemporary usage patterns, we propose a framework that defines clear minimum requirements for a system to be considered an agent while characterizing systems along a multidimensional spectrum of environmental interaction, learning and adaptation, autonomy, goal complexity, and temporal coherence. This approach provides precise vocabulary for system description while preserving the term's historically multifaceted nature. After examining potential counterarguments and implementation challenges, we provide specific recommendations for moving forward as a field, including suggestions for terminology standardization and framework adoption. The proposed approach offers practical tools for improving research clarity and reproducibility while supporting more effective policy development.