Designing lifecycle policies for AgentCore memory | Amazon Web Services
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 If you don’t actively manage these memories, your agents will accumulate outdated context, which can degrade response quality and create compliance risks for your deployment.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 dispute resolved four months earlier, treating it as active. Another agent repeated outdated deployment advice because its memory still contained a superseded runbook.
In this post, we introduce memory lifecycle management for AI agents: the practice of systematically scoring, consolidating, and pruning agent memories over time. We walk through a deployable architecture using AgentCore memory(a capability of Amazon Bedrock AgentCore), AWS Step Functions, and Amazon Bedrock to run a nightly lifecycle workflow. By the end, you will have an...
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