Designing lifecycle policies for AgentCore memory
AWS Machine Learning Blog · 人工智能
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 If you don’t actively manage these memories, your agents will accumulate outdated context, which can degrade response quality and create compliance risks for your deployment.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 Another agent repeated outdated deployment advice because its memory still contained a superseded runbook.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 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 . 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. Solution overview 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. Memory types Before designing lifecycle policies, we need a shared vocabulary for what agents remember. We categorize agent memory into three types, each with different retention requirements. Episodic memor