Amazon Web Services has published guidance on designing lifecycle policies for managing memories in long-running AI agents built with Amazon Bedrock AgentCore. According to the AWS AI blog, the framework addresses a growing challenge where AI agents accumulate outdated memories that can degrade output quality and create compliance risks over time.
The proposed solution involves implementing automated memory management through a nightly workflow using AWS Step Functions. According to Amazon, the lifecycle policy framework includes three key components: scoring agent memories to evaluate their relevance and accuracy, consolidating related memories to reduce redundancy, and pruning outdated or irrelevant information from the agent’s memory store.
Amazon has made the solution accessible to developers by providing a deployable AWS CDK stack, allowing organizations to implement memory lifecycle management without building the infrastructure from scratch. The framework is designed specifically for Amazon Bedrock AgentCore, AWS’s service for building and deploying AI agents at scale. According to the announcement, this approach helps organizations maintain agent performance while managing compliance requirements as their AI systems operate over extended periods.