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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Organizing Agents’ memory at scale: Namespace design patterns in AgentCore Memory

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