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ToolsAWS Machine Learning Blog
Groundhog Day for AI agents: Prompt caching patterns for using OpenAI models on Amazon Bedrock
AI 智能体的土拨鼠日：在 Amazon Bedrock 上使用 OpenAI 模型的提示缓存模式
AWS explains how prompt caching for OpenAI models on Amazon Bedrock cuts input cost and latency, but the real decision is whether your agent's repeated context justifies the added design complexity.
Judgment
Engineering leads building OpenAI-model agents on Amazon Bedrock must decide whether to add prompt-cache boundaries around repeated tool definitions and transcripts, accepting extra prompt-design and invalidation work only if their agents run long enough to reuse that prefix.
Basis
Claudio Mazzoni writes on the AWS Machine Learning Blog that agents resend tool definitions, instructions, prior tool calls, and results every step because the model has no memory between requests; by turn 20 the model rereads turns 1–19. The mechanism is prefix reuse: caching reduces repeated input processing only when the same earlier context is sent again. That makes the tradeoff architectural—stable prompt sections can be cached, but cache-aware ordering and lifecycle add design complexity, while short or rapidly changing contexts leave little to reuse.
Unclear The source does not give cache hit rates, cost savings, latency figures, minimum cacheable prefix lengths, or invalidation rules for OpenAI models on Bedrock.
Next Check the Bedrock prompt-caching documentation for the minimum cacheable prefix and TTL, then test one agent workflow with a stable tool-definition block.