Open-source harness cuts agent token usage by 2.7x: four strategies explained
Roger_M_Taylor · x · 2026-08-25
The article explains why LLM agent token consumption varies wildly: the same model on the same task can use nearly 3x more tokens depending on the harness. Extra usage comes from the code that decides what reaches the model's context on each call and how many calls are made. For example, a tool returning 50k tokens of JSON stays in context and is re-read at every step; a server exposing 50 tools puts all of them in the prompt by default. An optimized harness avoids this unnecessary cognitive load. The article covers what a production harness owns beyond the execution loop and four strategies to cut tokens. The open-source project TrueForge implements these ideas.
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