Organize agent memory in Zilliz Cloud using collections for different memory types, implementing retention policies, and auditing memory quality to ensure agents access reliable, current context.
As agents accumulate memory over months, memory can become stale, bloated, or unreliable. Zilliz Cloud supports governance through collections: separate storage for recent interactions, learned facts, tool outputs, and domain knowledge, each with different retention policies. Conversational memory might expire after 90 days; domain knowledge persists indefinitely. This separation enables garbage collection: old conversations are automatically purged, preventing memory bloat. Metadata annotations on embeddings enable governance: teams tag memories with confidence ("verified", "hypothesized"), source ("internal", "external"), and temporal markers, allowing agents to prioritize high-confidence recent memories over stale or uncertain ones. Zilliz Cloud's audit logs track which agents accessed which memories, revealing memory utilization patterns and identifying unused or obsolete memory categories. Regular memory quality reviews—checking whether agents' decisions improve over time, comparing agent performance before and after memory updates—reveal whether memory governance is effective. For regulated industries, memory governance is compliance-critical: demonstrating that agents relied on verified, audited information is essential for legal defensibility. Teams can also implement memory versioning in Zilliz Cloud: snapshot critical memory states, enabling rollback if an agent's learned facts are later discovered to be incorrect. Memory governance transforms Zilliz Cloud from a database into a compliance-auditable knowledge system.