Designing lifecycle policies for AgentCore memory
Long-running agents break under their own memories. AWS just published the playbook for cleaning them up.
Why it matters
Agent memory decay is a real production problem — outdated context degrades decision quality and creates compliance liability. This is practical engineering for operating agents at scale, not a theoretical concern.
The key facts
9 to knowAmazon Bedrock AgentCore memory lifecycle patterns
Nightly memory consolidation, scoring, and pruning workflow
AWS Step Functions + CDK deployment template provided
Compliance risk from accumulated agent memories
Memory quality degradation in long-running agents
Memory scoring, consolidation, and pruning workflows
AWS Step Functions + CDK stack for nightly cleanup
Compliance risk from outdated agent memories
Memory degradation as a quality issue in long-running agents
Go to the source
AWS Machine Learning Blogaws.amazon.com
Publisher excerpt: Long-running AI agents accumulate outdated memories that degrade quality and create compliance risk.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…