Zilliz Cloud provides built-in observability dashboards showing collection sizes, query latency percentiles, insert throughput, and memory usage — giving you visibility into how your agents consume vector storage over time.
Monitoring agent memory involves tracking both the volume of stored interactions and the query patterns agents use. Zilliz Cloud's metrics expose: how many vectors each collection holds, average and p99 query latency, insertion rate, and index build status. You can set alerts for collections approaching size limits or for latency spikes that could degrade agent performance.
For multi-agent systems, tag agent memories with metadata (agent ID, session ID, timestamp) to enable per-agent analytics. This lets you identify which agents are most memory-intensive, which topics are queried most frequently, and whether agent memories are growing faster than expected. Zilliz Cloud also provides collection-level TTL (time-to-live) settings, automatically expiring old memories and controlling storage costs. This is particularly useful for session-scoped agent memory that should not persist indefinitely.