models
Designing lifecycle policies for AgentCore memory
This article explains how to implement memory lifecycle policies for Amazon Bedrock AgentCore to manage long-running AI agents' memories, ensuring response quality and compliance. It covers memory types, policies such as TTL expiration, relevance scoring, and consolidation, and provides architecture and code examples for deployment.
AS1 NewsSource: aws.amazon.com
AMZNLong-running AI agents on Amazon Bedrock AgentCore can accumulate outdated memories that impact response quality and pose compliance risks. To address this, Amazon introduces memory lifecycle management, a systematic approach to score, consolidate, and prune agent memories over time.
The solution categorizes agent memories into three types: episodic, semantic, and procedural, each with different retention needs. Episodic memories capture past conversations and are high-volume, while semantic memories are distilled facts retained longer, and procedural memories encode operational workflows with the longest retention.
Three lifecycle policies are proposed: TTL-based expiration, relevance decay scoring, and LLM-based consolidation. TTL expiration automatically deletes memories older than a configurable threshold, with default values of 90 days for episodic memories and shorter durations for summaries. Relevance scoring evaluates memories based on recency and access frequency, assigning scores that determine their retention or pruning. Consolidation merges related memories into compact summaries using Amazon Bedrock, maintaining essential information while reducing redundancy.
The architecture involves a nightly workflow orchestrated by AWS Step Functions, triggered by Amazon EventBridge, which executes stages for pruning, scoring, consolidating, metrics emission, and output storage. The implementation leverages AWS CDK for infrastructure deployment, with Lambda functions handling each stage, and integrates AWS CloudTrail for access tracking.
This approach ensures that agent memories remain relevant and compliant, with configurable parameters to match specific use cases. The complete code and deployment instructions are available in the GitHub repository, enabling organizations to implement effective memory management for their AI agents.
The article details a method for managing AI agent memory lifecycle, which is crucial for maintaining response quality and regulatory compliance in enterprise AI deployments.