Where Does the Agent Live?
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AI agents are fundamentally a storage problem: the model, harness, and sandbox are all replaceable, but the agent's state (working files, memory, structured metadata, and provenance) is not. Today that state is typically scattered across Postgres, Redis, Pinecone, and other systems with no consistent snapshot boundary. The post argues for consolidating all agent state into a single object storage bucket, enabling copy-on-write forking so you can branch an agent's entire world before risky actions, parallel experiments, or eval runs. Using Tigris and Daytona as concrete examples, it walks through a practical shim: fork the bucket before each sandbox run, mount or access the fork with scoped credentials, then promote or discard after validation. This gives fleet-scale per-run isolation at near-zero cost since forks are metadata, not byte copies.
