Preprint Proposes TokenEconomizer: Cutting Coding Agent Token Costs by 18.4%-30.9% via Black-Box Control

Frontier coding agents like Claude Code, Codex, and GitHub Copilot CLI have sharply increased token costs in AI-assisted software engineering. Existing evaluations typically focus on isolated tasks, whereas real-world developers continuously build and maintain large-scale projects. This mismatch leads to recurring environment-dependent token costs that existing methods struggle to reduce.

The arXiv preprint "TokenEconomizer: Experience-Driven Black-Box Cost Control for Coding Agents" introduces TokenEconomizer, an external controller designed to address this issue. Without modifying the underlying model, the controller intervenes through three mechanisms: determining which operational micro-skills to inject, pruning persistently irrelevant tools, and intervening in unproductive failure loops.

The study shows that when paired with frontier models, TokenEconomizer reduces average token costs by 18.4% to 30.9% across Claude Code, Codex, and GitHub Copilot CLI, while maintaining comparable task success. These savings hold in every tested repository and agent-model setting, generalizing to diverse domains such as sequential web development and security auditing, where costs fall by 6.4% to 21.2% depending on task structure.

This work establishes adaptive black-box cost control as a promising direction for reducing token costs of coding agents in realistic settings. As these results come from a preprint (E2), they have not yet been independently reproduced, but they offer developers a concrete approach to optimizing costs without switching models.