Zilliz Cloud provides detailed query logs, performance dashboards, and inference attribution, enabling teams to debug agent decisions and optimize memory retrieval.
When agents fail or make unexpected decisions, debugging is difficult without visibility into memory retrieval. Zilliz Cloud logs every query: what embeddings the agent searched for, what was retrieved, with confidence scores. Teams can replay agent decision-making by examining these logs: "The agent decided to escalate to human support—what context did it retrieve that triggered this?" Detailed attribution reveals the specific memories that influenced each decision. Performance dashboards show query latency distributions, index utilization, cache hit rates, and throughput—revealing bottlenecks. If agent response time degrades, observability data pinpoints whether memory retrieval slowed or whether agent logic is inefficient. Teams can also set up alerting: notify oncall if Zilliz Cloud query latency exceeds thresholds, indicating potential issues. For multi-agent systems, cross-agent observability reveals which agents query which memories, enabling optimization of shared memory architecture. For example, if 90% of queries are for a small subset of embeddings, teams can optimize caching or replication strategies for that subset. Zilliz Cloud's observability is essential for understanding agent behavior in production, enabling continuous improvement.