Let’s say your customer support agent asks for “billing issues”, and gets back technical support tickets, sales conversations with receipt issues, and billing disputes all mixed. This is the retrieval precision wall that teams hit once their agents accumulate weeks of interaction history: similarity search finds everything that’s semantically close for this customer but does not scope it to the relevant dimensions you actually need: issue type, status, or time.

Amazon Bedrock AgentCore Memory is a fully managed memory service that gives AI agents the ability to remember and recall information across conversations. It organizes agent memory records into namespaces that define isolated scopes like clients/client-123, so each entity’s data stays separate. You can read the blog on Organizing Agents’ memory at scale: Namespace design patterns in AgentCore Memory to understand more about namespace organization. As memories grow, relevant signals drown in semantically similar but contextually irrelevant results, and namespace scoping alone cannot separate them.

Metadata filtering closes this gap. You can now layer fine-grained, attribute-based filters on top of namespace isolation that helps in scoping retrieval by business dimensions like priority, department, or time range before similarity search runs. In our evaluations across a 151-question test set built on a long-term memory benchmark (LoCoMo-style multi-session conversation), it showed improvement. The overall question-answering (QA) accuracy rose from 40% to 64% with metadata filtering enabled across all question types. The gain concentrates in the subset of questions that depend on contextual boundaries, such as time-bounded lookups, priority-based filtering, or department-scoped searches. For those questions, accuracy jumped from 16% to 69%.

In this post, you will learn how metadata works across configuration, ingestion, and retrieval, explore enterprise use cases including multi-agent and multi-tenant architectures, and discover best practices for implementation.

AgentCore Memory uses namespaces to organize and isolate memories along primary entity boundaries. You scope retrieval to a specific namespace like clients/client-123/sessionABC or patients/patient-456, so your agent does not accidentally retrieve another client’s or patient’s data. Namespaces provide the foundational layer of separation. Read more about it in the blog on Namespace design patterns.

As deployments scale, semantic search within a namespace hits some limits. Consider a financial services agent with a namespace per client that has accumulated six months of interaction history. When a relationship manager asks the agent to recall “portfolio rebalancing discussions” for a specific client, the namespace correctly scopes the search for that client’s memories. But the results span different investment strategies, time periods, and priority levels within that client’s history. The agent can’t distinguish a high-priority rebalancing conversation from last week from a routine inquiry three months ago. The information is semantically similar, but the context is entirely different.