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

TL;DR

This survey unifies computer science and economics to analyze token use in LLM agents, addressing trade-offs between output quality and economic costs across multiple system levels.

Contribution

It provides the first comprehensive framework combining economic theories with computer science to evaluate token economics in LLM agents.

Findings

Synthesizes literature across four levels: micro, meso, macro, and security.

Proposes a unified taxonomy for token economic analysis.

Outlines future directions like differentiable token budgets and dynamic markets.

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.