Observed Signal · Apr 24, 2026 · Technical Release · Source: DEV Community · Impact: 2/5 · Sentiment: Positive
Bedrock Prompt Caching Fix for Persistent Agent Memory
A developer connected agent-memory-daemon to OpenClaw running on Amazon Bedrock AgentCore Runtime and discovered that injecting persistent memory into the user message prevented Bedrock from serving that content from prompt cache. Bedrock caches a stable prefix of a request; placing an 18KB MEMORY.md at the end of the user message caused each turn to diverge and pay full input rates. Moving the curated memory into a system message (part of the stable prefix) allowed Bedrock to cache the memory block, raising cache hits (reported 99% hit) and dramatically cutting uncached input costs (author reports roughly a 90% discount on the biggest bill line item). The post explains architecture, caching behavior, the one-line fix, and operational takeaways for running persistent memory with serverless agents on Bedrock.
Practical developer-level guidance on LLM prompt caching and memory injection that can materially reduce inference costs for serverless agent deployments on Amazon Bedrock, but it's a narrow technical optimization rather than industry-wide policy or platform change.
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Key Takeaways & Evidence Grounding
- Author integrated agent-memory-daemon with OpenClaw on Amazon Bedrock AgentCore Runtime to provide persistent memory via an ~18KB MEMORY.md file synced from S3.
- Bedrock prompt caching caches a stable prefix of the request; variance after the prefix causes the remainder to be billed as uncached input.
- Placing memory in the user message caused repeated uncached input (~31.91M tokens in three days); moving the memory to a system message made it cacheable and shifted traffic to cheaper cache reads (~12.69M cache reads).
- Claude Haiku 4.5 supports prompt caching with a 5-minute TTL via cacheRetention:'short'; cache reads are billed at ~10% of regular input while cache writes can cost more (noted ~1.25x on Haiku 4.5).
- After the fix the author observed OpenClaw usage showing 99% cache hit and 67k tokens cached versus 715 new tokens in a sample report.
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