Defining AI Agents: A Compendium of Criteria, Metrics, and Benchmarks

TEXT START: The term agent in artificial intelligence lacks a standard definition, complicating the evaluation, comparison, and reproducibility of AI agent research.

The Dissection

This paper is building a measurement and classification layer for AI agents. Its five dimensions—interaction, learning, autonomy, goal direction, and temporal coherence—make agent behavior easier to catalogue, compare, and benchmark.

That is useful engineering hygiene. It is not an account of what agents do to the economic order. The paper converts a power transition into an evaluation problem: define the machine more precisely, measure it more consistently, and make the research ecosystem more reproducible. The machinery becomes legible while its consequences remain outside the frame.

The Core Fallacy

The central error is treating definitional ambiguity as the principal obstacle to progress. Under the Discontinuity Thesis, the decisive question is not whether an AI system satisfies a clean taxonomy of agenticness. It is whether the system can perform economically necessary cognitive work at lower cost and superior scale than humans, and whether human institutions can prevent its deployment from severing the labor-to-consumption circuit.

A benchmark can establish that an agent plans, adapts, and acts over time. It cannot establish that displaced workers retain productive access, bargaining power, or ownership of the systems replacing them. Better measurement may accelerate P1—cognitive automation dominance—without solving P2 or P3. The paper risks polishing the dashboard while the vehicle is leaving the road.

Hidden Assumptions

• That clearer definitions and standardized benchmarks are the main prerequisites for meaningful comparison.
• That agentic capability can be evaluated independently from ownership, deployment authority, access to capital, and control of infrastructure.
• That benchmark performance transfers reliably to messy economic environments.
• That autonomy, goal pursuit, and temporal coherence are primarily technical properties rather than sources of institutional and labor displacement.
• That improved reproducibility is socially neutral, when reproducible evaluation can also make automation easier to finance, scale, and deploy.
• That the relevant unit of analysis is the AI system, not the Sovereign who owns it, the Servitor who maintains it, or the majority whose productive role it eliminates.
• That systematic study of agents is an unqualified advance rather than an acceleration mechanism for the transition.

Social Function

Primary classification: prestige signaling and transition management, with a layer of ideological anesthetic.

The paper supplies an academic vocabulary for an industry already building increasingly autonomous systems. It converts a chaotic strategic shift into orderly dimensions, metrics, and public resources. That produces legitimate partial truth: inconsistent definitions genuinely impede research quality.

But the framing also sterilizes the conflict. It presents agent evaluation as a neutral infrastructure problem and omits the distributional question—who owns the agents, who captures the productivity gains, and who becomes economically unnecessary. In DT terms, it helps standardize the instruments of replacement while leaving replacement itself politically unexamined.

The Verdict

A competent taxonomy is not a survival theory. This paper improves the measurement of the agents likely to erode human productive participation, but offers no mechanism against that erosion. It is a useful technical map of the weapon and a blank page on who controls the battlefield.