Agent readiness can cut repeated token work

Factory’s agent readiness approach aims to reduce avoidable AI coding-agent token use by identifying missing repository documentation, setup instructions, validation commands, and engineering foundations that otherwise force agents to repeatedly reconstruct context. Its readiness reports and AGENTS.md support are intended to preserve accurate, concise project knowledge such as tested build commands, environment requirements, repository boundaries, and exceptions, while emphasizing that instructions must be verified in their intended environment. Organizations are encouraged to measure representative tasks before and after readiness improvements using comparable models, environments, and acceptance criteria, tracking failed commands, repeated repository reads, elapsed time, reviewer corrections, and model usage. The article cautions against claiming token savings solely from readiness scores or conflating them with other changes, noting that a cited 60% reduction in context-switching time at Nav concerns human workflow rather than measured token savings. It recommends prioritizing recurring issues affecting many tasks and confirming improvements through subsequent task outcomes rather than assuming success after documentation is added.

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