ResearchResearch paperReinforcement Learning · Safety & Alignment · Agents & Tool Use1 source · Oct 15, 2026

TokenEconomizer: Experience-Driven Black-Box Cost Control for Coding Agents

To minimize these costs, we introduce TokenEconomizer, an external controller that cuts environment-specific token costs over a sequence of tasks.

Advances in frontier coding agents have sharply increased the token costs of AI-assisted software engineering.
This mismatch motivates us to evaluate agents over sequences of realistic coding tasks, where we find that agents exhibit recurring environment-dependent token costs that existing methods struggle to reduce.
Across Claude Code, Codex, and GitHub Copilot CLI paired with frontier models, TokenEconomizer reduces the average token cost by 18.4-30.9% with comparable task success.
Our work establishes adaptive black-box cost control as a promising direction for reducing token costs of coding agents in realistic settings.

[1]TokenEconomizer: Experience-Driven Black-Box Cost Control for Coding Agents
arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 15, 11:06 PM
To minimize these costs, we introduce TokenEconomizer, an external controller that cuts environment-specific token costs over a sequence of tasks.
Advances in frontier coding agents have sharply increased the token costs of AI-assisted software engineering.

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