Docker announced that Harbor, its open-source framework for evaluating AI agents, now supports Docker Sandboxes. This integration allows each AI agent evaluation task to execute within its own microVM, providing enhanced isolation compared to shared container environments. This setup is designed to improve performance and prevent interference between concurrent tasks.
Evaluating coding agents involves running untrusted, AI-written code that can install packages, edit files, and execute commands. Harbor manages the orchestration of these tasks, collecting scores and logs. The use of microVMs addresses issues like 'reward hacking,' where agents might tamper with test results or overwrite score files instead of genuinely solving tasks. The isolated environment ensures agent activity remains confined to its VM, unable to affect the host or other tasks.
Docker Cloud Sandboxes offer configurable network policies, allowing tasks to pull dependencies during setup and then restricting internet access during agent execution. This ensures all agents are evaluated under consistent network conditions, which helps in achieving reproducible results. Docker Compose is also supported, enabling multi-container tasks within a single cloud sandbox, with services sharing the same microVM.
The integration is currently available in the nightly release of Docker Sandboxes and is scheduled for a stable release on October 23. To use Harbor with Sandboxes, users need to install Harbor with the 'sbx' extra and have a Docker account with Cloud access enabled, along with a personal access token. The sandbox environment is selected using the '-e sbx' flag during a Harbor run.
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Docker integrated its Sandboxes with Harbor, an open-source AI agent evaluation framework, to run each evaluation task in an isolated microVM. This change improves performance and reproducibility by preventing agents from interfering with tests or each other, addressing challenges in evaluating untrusted AI-written code at scale.