Temporal Sandbox Orchestration Harness

Teams building agents that use sandboxes are all writing some version of the following functionalities:

• Provisioning on demand. The agent needs a sandbox. Something has to create it, configure it, and hand back a connection.
• Driving execution from agent intent. The LLM decides to run pytest. That decision has to reach the sandbox, execute, and return results.
• Persisting state across long runs. A coding agent working for hours or days needs its workspace to survive process restarts, worker migrations, and infrastructure failures.
• Recovering from sandbox failures. The agent's conversation state is fine (it's in the workflow), but the sandbox is gone. Something has to re-provision and restore.
• Cleaning up. Sandboxes are meant to be temporary environments, but what temporary means is different for every use case. Tearing the sandbox down at the appropriate time for your use case is critical to providing the right level of persistence while balancing spend on sandbox compute.