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Research indicates that AI agents operating on unhealthy codebases consume 35-45% more tokens and generate lower-quality outputs compared to those working on maintainable code in C++, Java, and Python. This inefficiency stems from increased model confusion when processing code with high defect density, leading to higher operational costs and reduced task success rates. Establishing workflows that enforce code health metrics is essential for optimizing AI agent performance and reducing unnecessary inference spend.

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Unhealthy Code is Burning Your Token Usage - Here's the Data

CodeSceneMay 28, 20265 min read

Our research across C++, Java, and Python shows that agents working on unhealthy codebases consume 35-45% more tokens to complete the same tasks. That’s in addition to the increased defect risk covered in earlier research. You're not just getting worse output. You're paying significantly more for it. Knowing that your unhealthy code burns excess tokens is useful. Having a workflow that prevents it from getting worse with every agent commit is what changes the outcome.

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