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Lakebase Search is a hybrid vector and full-text retrieval system integrated into Lakebase, now in beta on AWS and Azure. It utilizes two Postgres extensions: lakebase_vector and lakebase_text, allowing agents to operate on a single data backend. Agents manage four times more databases than human users and require real-time access to indexed data. The system features a tiered architecture that stores cold data in cost-effective object storage while keeping active data in local NVMe, significantly reducing costs. The lakebase_vector extension offers 32x compression for vectors, allowing a billion vectors to fit into under 10GB of RAM. The lakebase_text extension provides BM25 relevance ranking without high RAM usage. Benchmarking shows that Lakebase Search can efficiently handle large-scale workloads, achieving high recall and low latency with reduced resource requirements compared to traditional architectures. The system allows for continuous search experimentation and dedicated retrieval engines for each agent, enhancing operational efficiency and scalability.