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.
What happened
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 If you don’t actively manage these memories, your agents will accumulate outdated context, which can degrade response quality and create compliance risks for your deployment.
Where the sources line up
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 Another agent repeated outdated deployment advice because its memory still contained a superseded runbook.
Practical impact for readers
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 AWS Cloud Development Kit (AWS CDK) stack and a framework for managing agent memory as a managed resource. The complete code is available in the GitHub repository .
Who should pay attention now
This solution targets agents that accumulate high volumes of interaction data over weeks or months, such as customer support agents, sales advisors, and IT helpdesk bots. For lower-volume agents like personal assistants, you might start with time-to-live (TTL) expiration and General Data Protection Regulation (GDPR) compliance alone. All thresholds are configurable to match your agent’s needs.
What is still unclear
This solution combines a shared memory taxonomy with three lifecycle policies that run as a nightly workflow. We begin with the memory types that shape those policies.
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