Sometimes Your AI Agent Burns More Energy Optimizing Code Than the Code Will Ever Save
TL;DR AI coding agents burn 100,000+ tokens per task. When the task is “optimize this code’s performance,” the agent itself often costs more energy than the optimized code will ever save. We built GA4GC — Greener Agent for Greener Code — using NSGA-II to tune the agent’s own configuration against three objectives: code correctness, code speedup, and agent runtime. On a mini-SWE-agent powered by Gemini 2.5 Pro on the SWE-Perf benchmark, we got 37.7% runtime reduction while also improving correctness, with a 135× hypervolume improvement over defaults. Bonus finding: temperature is the single most important knob, and LLM hyperparameters control quality while agent constraints control cost — they can be tuned almost independently. The energy paradox nobody talks about Here’s a thing that should be obvious but isn’t: when you ask an AI agent to optimize the performance of your code, the agent’s own execution costs energy. A lot of energy. Often more than the code it’s optimizing will ever save. ...