TokenEconomizer cuts coding-agent token costs by 18.4-30.9% via black-box control

[HF Papers] Frontier coding agents have sharply raised the token costs of AI-assisted software engineering, and their token efficiency is usually measured on isolated tasks rather than the continuous build-and-maintain work developers do, according to the paper TokenEconomizer: Experience-Driven Black-Box Cost Control for Coding Agents. Aaron J. Li and co-authors propose TokenEconomizer, an external controller that cuts environment-specific token costs over a sequence of tasks by deciding which operational micro-skills to inject, which persistently irrelevant tools to prune, and when to intervene in unproductive failure loops. The authors report that across Claude Code, Codex and GitHub Copilot CLI paired with frontier models, the method reduces average token cost by 18.4-30.9% with comparable task success, with savings holding in every repository under every tested agent-model setting and generalizing to domains such as sequential web development and security auditing, where costs fall by 6.4-21.2% depending on task structure. The work is a preprint and its findings have not been peer reviewed.