AgentsSeptember 4, 2026via AWS Machine Learning Blog
Designing lifecycle policies for AgentCore memory
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Agent memory decay is a real production problem — outdated context degrades decision quality and creates compliance liability.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.
Key signals
- Amazon 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- Compliance risk from outdated agent memories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
The hook
Long-running agents break under their own memories. AWS just published the playbook for cleaning them up.
Long-running AI agents accumulate outdated memories that degrade quality and create compliance risk.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 stac…