Replit's snapshot engine enhances the safety of AI agents through its unique compute and storage architecture, which allows for isolated and reversible changes. This design enables developers to experiment rapidly by cloning environments and making adjustments without fear of permanent disruption. The introduction of the Replit Agent in 2024 highlighted the importance of these features, as direct access to code and databases poses risks. The snapshot system allows users to revert changes in their code or databases, minimizing the potential for irreversible errors. The Bottomless Storage Infrastructure, launched in 2023, underpins this system. It offers virtual block devices stored in Google Cloud Storage, enabling fast and efficient copying of filesystems. This setup uses Copy-on-Write technology to facilitate quick snapshots and versioning of entire applications. Each version can be restored easily, providing robust disaster recovery options. Code changes made by the Agent are tracked through Git, ensuring that users can roll back to any previous state. The design even accommodates potential corruption of the Git state by allowing recovery from immutable backups. Managing databases alongside code changes is critical, as they evolve together. Replit separates production and development databases, granting AI agents access only to the latter. The system can create versioned, forkable databases using a local PostgreSQL instance, which streamlines the checkpointing process for both code and data. This capability allows users to roll back or fork databases quickly, maintaining performance and reducing overhead. Looking ahead, Replit plans to leverage this technology to create safe, isolated environments for AI agents to test changes. These sandbox environments will enable agents to experiment with less restriction, making temporary adjustments without risking production data. Multiple agents can work in parallel, each exploring different solutions to the same problem. This approach harnesses the non-determinism of language models, promoting diverse outcomes from identical starting points.