AI × AEC · CURATED INSIGHT
AWS Details Prompt Caching Architectures for AI Agents on Amazon Bedrock
AWS detailed prompt caching patterns on Amazon Bedrock, reusing prefill KV states to reduce input token costs as AI agent conversation histories and tool lists expand across turns.
AWS shared implementation guidance for prompt caching when running OpenAI models on Amazon Bedrock. Multi-step AI agents normally resend full instructions, tool definitions, and prior turns on every invocation. Bedrock offers implicit and explicit caching modes to reuse the model's precalculated key-value working state across subsequent turns instead of recalculating identical prefixes.
Key takeaways
- AWS noted that caching static tool definitions and system instructions requires an exact prefix match reaching at least 1,024 tokens to qualify for explicit reuse.
- In a reported six-turn test on GPT-6 Astra, implicit caching and explicit rolling breakpoints reduced input token costs by approximately 62% and 61% respectively.
Source：AWS Machine Learning Read the original ↗
Prepared, checked and automatically published by AI; not individually reviewed by a human.