Evaluating memory architectures for production AI agents reveals significant trade-offs between retrieval complexity and operational scalability. Benchmarks conducted on a MacBook Pro M2 demonstrate that while vector stores like FAISS offer superior raw performance for fuzzy similarity—achieving sub-10ms top-5 retrieval latency for 10,000 vectors—they struggle with structured, multi-hop, or time-sequenced queries. In contrast, graph databases like Neo4j excel at native relationship traversal, such as 3-hop queries, but suffer from high operational costs; benchmarks show a 5,000ms latency for 5,000 event inserts and significantly slower retrieval compared to other methods. SQL databases, specifically SQLite, emerge as a robust hybrid solution for production workloads. While SQLite exhibits a slightly higher 25ms retrieval latency compared to FAISS, it provides the necessary flexibility for complex, multi-constraint queries involving metadata and time ranges. The data suggests that while vector databases are ideal for simple semantic similarity, they fail when agents require context chaining or strict ordering. For production systems where hybrid recall—combining structured metadata with semantic search—is essential, a SQL-based approach offers the best balance of debuggability, schema flexibility, and scaling potential.