AWS详解Bedrock智能体提示缓存
AWS 机器学习博客介绍了 Amazon Bedrock 提示缓存如何在智能体的每一步中复用已处理的指令、工具定义和对话历史,避免反复处理完整上下文。文章涵盖隐式与显式缓存断点、稳定前缀、附加工具、缓存资格及写入成本。AWS 在一个六轮工作负载中报告,GPT-6 Astra 采用隐式缓存可降低约62%的输入成本,显式滚动断点则降低约61%,同时强调实际节省取决于工作负载。
AWS 表示这些模式可在保持智能体上下文连续性的同时减少重复输入处理,但报告的节省幅度取决于具体工作负载和缓存复用情况。
Every time you spin off your agent, it has to re-read its whole life story before performing an action. Here’s how to make it remember without having to feed it the same context again.
In the movie Groundhog Day, Bill Murray’s character re-lives the same February 2 every day. He wakes up to the same song playing on the radio, in the same inn, and the same small town, again and again for what some movie fans estimate to be 34 years.
Nothing he accomplished the day before carries over unless he works it out again from scratch. AI agents live in a version of Groundhog Day. Every time an agent takes a step (calls a tool, reads the result, decides what to do next), it has to send the model its entire story so far to maintain continuity: the tool definitions, the instructions, every previous tool call, and every result. The model only predicts the next token based on the input you provide. It has no internal memory between requests, so it must read all of it again. By turn 20 of a coding session, the model re-reads turns 1–19, and it’s you who pays for every one of those tokens, again and again.
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