Agent costs are driven by repeated prompts, and prompt caching is often lost by accident

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Most of an AI agent's token spending goes to resending the same prompt, including instructions, tool definitions, and conversation history, on every model call. Providers offer a discounted rate for this through prompt caching, but it only works if the start of the prompt is identical to the previous request. Common mistakes such as embedding a current timestamp, rewriting task counters, reordering tool definitions, or trimming history each turn break the cache and force full-price processing. The article points to an open issue in OpenClaw's tracker where heartbeat wake-ups rewrote the cached prefix, making each one cost a full cache write. Its recommended fixes are keeping the system prompt byte-identical, only appending to history, compacting long conversations once rather than every turn, and avoiding polling loops, with an example suggesting roughly 85% savings on repeated input.

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