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Agents interacting with traditional OLTP databases face bottlenecks at the storage layer due to data movement required for operations like deploying instances and managing replicas. Object storage solutions, such as Amazon S3, provide a more efficient and cost-effective alternative. The text discusses two OLTP models: data-centric and transaction-centric. The transaction-centric model emphasizes the importance of transaction history, which Postgres retains through its write-ahead log (WAL). The WAL records every modification before it is committed, allowing for the reconstruction of the database state as of a specific point in time. Lakebase Postgres introduces a two-layer architecture separating compute and storage. The compute layer handles SQL operations, while the storage layer ensures data durability and historical integrity. The WAL is streamed to safekeepers for replication, and the pageserver transforms WAL into materialized pages stored in object storage. This architecture allows for efficient read and write operations without direct interaction with object storage during queries. Key features of Lakebase Postgres include branching, which allows agents to create pointers to specific LSNs without copying data; instant restore capabilities that enable point-in-time recovery; time travel queries for accessing historical data; and read replicas that do not require data duplication. The system also supports scaling down idle compute nodes and eliminates the need for separate copies of data for transactional and analytical processing by storing a single durable copy in object storage. Overall, Lakebase Postgres enhances agent interactions with the database by leveraging object storage as a foundational element.