Organizing Agents’ memory at scale: Namespace design patterns in AgentCore Memory
Machine Learning Blog
This article explains how to design namespace hierarchies in Amazon Bedrock AgentCore Memory for organizing AI agent long-term memory at scale.
- Namespaces are hierarchical paths organizing memory records, similar to file system directories
- Scope semantic and preference memories to actors for cross-session consolidation
- Scope summaries and episodes to sessions since they're conversation-specific
- Use namespace field for exact match retrieval; namespacePath for hierarchical retrieval
- Three retrieval APIs: RetrieveMemoryRecords (semantic search), ListMemoryRecords (enumeration), GetMemoryRecord (direct lookup)
- IAM policies control namespace access using bedrock-agentcore:namespace and bedrock-agentcore:namespacePath condition keys
- Design patterns support multi-tenant isolation and granular access control
Effective namespace design enables precise memory retrieval, clean data isolation, and IAM-based access control for AI agents.
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