Token Economics for LLM Agents: A Dual-View Study from Computing and Economics

2026-05-09

AI Summary

This work addresses the exponential token consumption in large-model agents, which creates critical bottlenecks in computation, collaboration, and security, necessitating a systematic trade-off between output quality and economic cost. The paper introduces, for the first time, a unified token economics framework that conceptualizes tokens as factors of production, media of exchange, and units of account. Integrating neoclassical firm theory, transaction cost economics, principal-agent models, and mechanism design, it establishes a four-dimensional analytical system encompassing micro-level single-agent behavior, meso-level multi-agent systems, macro-level ecosystem dynamics, and security considerations. By leveraging factor substitution, dynamic pricing, and differentiable budget constraints, the framework systematically optimizes collaborative efficiency and resource allocation, while charting promising directions such as dynamic markets to lay the theoretical foundation for scalable agent systems.

Abstract

As LLM agents evolve, tokens have emerged as the core economic primitives of Agentic AI. However, their exponential consumption introduces severe computational, collaborative, and security bottlenecks. Current surveys remain fragmented across system optimization, architecture design, and trust, lacking a unified framework to evaluate the fundamental trade-off between output quality and economic cost. To bridge this gap, this survey presents the first comprehensive survey of Token Economics. By unifying computer science and economics, we conceptualize tokens as production factors, exchange mediums, and units of account. We synthesize existing literature across a four-dimensional taxonomy: (1) Micro-level (Single Agent): Optimizing budget-constrained factor substitution via neoclassical firm theory. (2) Meso-level (Multi-Agent Systems): Minimizing collaboration friction using transaction cost and principal-agent theories. (3) Macro-level (Agent Ecosystems): Addressing congestion externalities and pricing via mechanism design. (4) Security: Internalizing adversarial threats as endogenous economic constraints. Finally, we outline frontier directions, including differentiable token budgets and dynamic markets, to lay the theoretical foundation for scalable next-generation agent systems.

Problem

• Token Economics
• LLM Agents
• Economic Cost
• Computational Bottleneck
• Quality-Cost Trade-off

Innovation

• Token Economics
• LLM Agents
• Multi-Agent Systems
• Mechanism Design
• Neoclassical Firm Theory