Amazon released a guide to memory lifecycle management for agents in Bedrock AgentCore
Amazon published a guide and AWS CDK architecture for memory lifecycle management for agents in Bedrock AgentCore - a combination of TTL expiration, scoring and consolidation that addresses the problem of agents referring to outdated context.
Amazon published a guide on the AWS Machine Learning Blog to memory lifecycle management ("memory lifecycle management") for long-lived agents built on Amazon Bedrock AgentCore. According to the company, agents without active memory management accumulate outdated context, which degrades response quality and creates compliance risks. As an example, the company cites a customer support agent that referred to a billing dispute resolved four months earlier as if it were still active, and another agent that repeated outdated deployment instructions from an outdated runbook.
The proposed solution combines three policies - TTL expiration, scoring and memory consolidation - run as a nightly workflow through AWS Step Functions and Amazon Bedrock. According to the company, Bedrock AgentCore memory does not have built-in automatic deletion based on TTL, but provides system timestamps that support BEFORE/AFTER filtering through ListMemoryRecords, which the solution uses to identify and delete old records. Recommended TTL differentiation: 30-60 days for summary memories, 6-12 months for semantic memories, no time limit for procedural memories; the default value for episodic memories is 90 days.
Memory scoring uses a weighted formula combining three components - time since creation, time since last access and access frequency - with configurable default weights of 0.4 / 0.35 / 0.25 and a pruneDays parameter that determines after how many days an unused memory falls below the relevance threshold. The company recommends different pruneDays values by agent type: 7 days for real-time support bots, 21 days for sales/onboarding agents, 45 days for general assistants, 90 days for IT helpdesk/ops agents and 180 days for legal and compliance advisors. The solution is available as an AWS CDK stack with code in a GitHub repository, aimed primarily at agents with high interaction volumes; for low-volume agents such as personal assistants, the company recommends starting with just TTL expiration and GDPR compliance.
Details can be found in the source article.
Why it matters
For developers building agents on Bedrock AgentCore, this is a ready-made architectural pattern with configurable parameters that addresses a real problem - agents referring to outdated or long-resolved information as current, leading to incorrect responses and potentially also breaches of compliance requirements due to unlimited data accumulation in memory.
Two audiences, two different impacts
What this means
For individuals
A developer working with Amazon Bedrock AgentCore memory can directly use the recommended values (differentiated TTL by memory type, pruneDays by agent type) instead of designing custom lifecycle logic from scratch.
For a business
Companies running long-lived agents (customer support, sales, IT helpdesk) can use systematic memory deletion and consolidation to reduce both the risk of incorrect responses based on outdated context and the risk of breaching compliance requirements such as GDPR due to unlimited data accumulation.
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