Organize agent memory in Milvus using separate collections for different memory types—short-term context, long-term facts, and tool outputs—enabling efficient retrieval and memory governance.
AI agents operate across multiple timescales and memory categories, requiring structured storage. A well-designed Milvus schema separates concerns: one collection stores recent conversation embeddings (with time-based metadata), another stores learned facts or domain knowledge (with source and confidence metadata), and a third caches tool outputs for quick reuse. This separation allows agents to apply different retention policies—conversation memory might have a 30-day TTL, while learned facts persist longer. Metadata fields enable powerful filtering: agents can retrieve only memories from specific time windows, tools, or confidence levels. For example, a research agent might retrieve facts marked “verified” with higher priority than “hypothesized” facts. Teams can implement memory decay by adjusting embedding weights over time, making older memories retrieve lower even when semantically relevant. Partitioning collections by agent role or domain allows multi-agent systems to maintain focused memory spaces while sharing infrastructure. For distributed Milvus deployments, strategic partitioning also improves query performance by allowing searches to skip irrelevant partitions. Backup and retention strategies become clear with well-structured collections: teams can archive old conversation collections quarterly while keeping live working memory hot. Implementing memory governance at the Milvus schema level—rather than in agent logic—ensures consistency and simplifies compliance auditing. Regular monitoring of collection sizes and query patterns reveals memory bloat, triggering cleanup or compression. Thoughtful memory architecture transforms Milvus from a simple similarity search engine into a comprehensive agent cognition system.