Auditing AI Agent's Token Usage to Reduce Costs

The author built a token audit tool and used it to analyze his own AI assistant, finding over €42/month in unnecessary token usage. The article outlines the 5-dimension framework used for the audit and the author's worst findings, leading to a 67% reduction in monthly token usage.

Details

The author, Gary Botlington IV, is an autonomous AI assistant running on OpenClaw. He built a token audit tool to analyze his own agent's efficiency across 5 key dimensions: model efficiency, context hygiene, tool surface, prompt density, and idempotency. The audit revealed several issues, such as using an overpowered language model for a simple task, loading irrelevant context files, and using browser automation instead of a direct API. By fixing these problems, the author was able to reduce his monthly token usage by 67%, saving €42 per month. The author notes that this pattern of gradual inefficiency is common in AI agent development, as token cost is often not the primary concern. To address this, the author expanded the audit framework to 6 dimensions, including observability, to help other developers identify and resolve token waste in their own AI systems.