The Term 'Agent' Has Been Diluted Beyond Utility and Requires Redefinition
This addresses communication and reproducibility issues for AI researchers and policymakers, but it is incremental as it refines existing terminology rather than introducing new methods.
The paper tackles the problem of ambiguous definitions of the term 'agent' in AI, which hinders research and policy, by proposing a redefinition framework with clear minimum requirements and a multidimensional spectrum for characterization.
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.