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# AWS puts a clock on agent memory: 90 days for episodic, no expiry for procedural
- URL: https://globalfeed.ai/en/aws-puts-a-clock-on-agent-memory-90-days-for-episodic-no-expiry-for-procedural/
- Published: 2026-09-06T14:10:57.000Z
- Updated: 2026-09-06T14:10:57.000Z
- Description: AWS has published guidance on lifecycle policies for the memory of long-running agents. Memory is split into three kinds with a separate retention period for each, and a dedicated handler deletes every record belonging to one user for right-to-be-forgotten requests. It also gives a cost figure: 100,
- Author: GlobalFeed Editor
- Tags: AWS, AgentCore, agent memory, data retention, GDPR, x-awscloud, dil-en, elle, video, x-gitti

AWS has published [guidance](https://aws.amazon.com/blogs/machine-learning/designing-lifecycle-policies-for-agentcore-memory/?ref=globalfeed.ai) on how long a long-running AI agent should keep what it remembers. The post went up on 4 September; it splits agent memory into three kinds, proposes a separate retention period for each, and ties the whole thing to working code. It looks technical, but it is a retention policy question, which makes it a legal and cost question.

## Three kinds of memory, three periods

- **Episodic**, the conversation records: expire these first, default 90 days.
- **Semantic**, extracted facts and preferences: 6 to 12 months.
- **Procedural**, learned workflows and tool-use patterns: the longest; AWS suggests considering no expiry at all.

## Three policies

The first is expiry by time to live, defaulting to 90 days for episodic records and 30 to 60 days for summaries. The second is relevance decay: creation recency is weighted 0.4, last-access recency 0.35 and access frequency 0.25, with the access count saturating at 50\. The third is model-assisted consolidation, merging related memories into a single compact entry.

The pruning window in the post's table varies by the agent's job: 7 days for a real-time support bot, 21 for sales and onboarding, 45 for a general assistant, 90 for an IT helpdesk, and 180 for a legal and compliance advisor.

## Compliance and cost

The post defines a dedicated handler that deletes every memory belonging to one user for right-to-be-forgotten requests, and leaves the audit trail in CloudTrail. On cost it gives one figure: 100,000 memories can reach 50 to 100 dollars a month. The reference build is Step Functions, five Lambda functions and an EventBridge rule firing nightly at 02:00 UTC; the code sits in the [aws-samples repository](https://github.com/aws-samples/sample-memory-lifecycle-policies-for-bedrock-agentcore?ref=globalfeed.ai).

## For the corporate reader

Agent memory holding personal data is a record system under data protection law. Putting an agent into production without a retention period, a deletion procedure and an audit trail means accumulating an archive that never shows up in your data inventory. The AWS table is a good starting point for that conversation; the periods still have to be mapped onto your own retention policy.

## Sources

- **Primary source:** AWS Machine Learning blog, 4 September: [aws.amazon.com/blogs/machine-learning](https://aws.amazon.com/blogs/machine-learning/designing-lifecycle-policies-for-agentcore-memory/?ref=globalfeed.ai)
- [Sample code, aws-samples repository](https://github.com/aws-samples/sample-memory-lifecycle-policies-for-bedrock-agentcore?ref=globalfeed.ai)