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Designing lifecycle policies for AgentCore memory

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Long-running AI agents accumulate outdated memories that degrade quality and create compliance risk. Learn how to design memory lifecycle policies for Amazon Bedrock AgentCore: scoring, consolidating, and pruning agent memories on a nightly AWS Step Functions workflow, with a deployable AWS CDK stack.

Extracto de la fuente

Memory lifecycle policies help long-running agents on Amazon Bedrock AgentCore stay effective by systematically managing what they remember and forget. Your agent generates memories from every conversation it conducts. If you don’t actively manage these memories, your agents will accumulate outdated context, which can degrade response quality and create compliance risks for your deployment. After months of production use, problems emerge. We observed a customer support agent reference a billing…

overfeed.news indexa y enlaza. Publicamos un extracto breve; el artículo completo sigue en AWS Machine Learning Blog.

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Designing lifecycle policies for AgentCore memory — overfeed.news