Designing lifecycle policies for AgentCore memory

This article presents a framework for managing Amazon Bedrock AgentCore agent memories through lifecycle policies that systematically score, consolidate, and prune memories to maintain agent effectiveness and compliance.

• Categorizes agent memory into episodic (conversation records), semantic (distilled facts), and procedural (learned workflows) types with different retention requirements
• Implements three complementary lifecycle policies: TTL-based expiration (default 90 days), relevance decay scoring using creation/access recency and frequency, and LLM-based consolidation to merge related memories
• Uses AWS Step Functions to orchestrate nightly workflows combining Lambda functions, CloudTrail access tracking, and Amazon Bedrock for memory consolidation
• Provides configurable parameters (pruneDays, relevance threshold, scoring weights) tuned for different agent archetypes from real-time support bots to legal advisors
• Includes regression testing using AgentCore Evaluations to verify pruning doesn't degrade response quality and GDPR deletion handlers for compliance
• Delivers complete AWS CDK stack and Python code for immediate deployment with cost estimates ($0.01–$0.02 per run for 1,000 memories)

The solution enables production agents to maintain memory quality and compliance by treating memory as a managed resource with systematic lifecycle governance.