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

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…
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