Editorial illustration for Harbor Framework Launches Sandboxed Agent Execution Across Multiple Cloud Platforms
Harbor Framework: Multi-Platform AI Agent Sandbox Tool
Harbor Framework Enables Sandbox Agent Execution on Docker, Modal, Daytona
The barrier between an agent and the real world is a sandbox. Without one, experiments are brittle, results are irreproducible, and scaling is a nightmare. Harbor tears that barrier down.
It’s a framework purpose-built for evaluating agents inside containerized environments at scale, supporting Docker, Modal, Daytona, E2B, and Runloop as sandbox providers. No more wrestling with infrastructure. Harbor handles automatic test execution on benchmark tasks, runs reward scoring to verify completion, and ships with pre-built datasets like Terminal Bench.
We built a backend that wraps shell-execution capabilities with file-system tools, `edit_file`, `read_file`, `write_file`, `ls`, all on top of raw shell commands. The result? A clean, measurable way to see how well agents operate in computer environments.
Some tasks, like cobol-modernization, demand over a hundred tool calls and stretch nearly ten minutes. Others, like path-tracing or chess-best-move, finish in seconds. Every task carries its own verification logic, awarding a crisp 0 or 1 reward score.
We ran the DeepAgents CLI with claude-sonnet-4-5 on Terminal Bench 2.0 across two trials: 44.9% and 40.4%, a mean of 42.65%. That’s a baseline. And it’s just the start.
Harbor is a framework for evaluating agents in containerized environments at scale, supporting Docker, Modal, Daytona, E2B, and Runloop as sandbox providers.
The real promise here isn’t the 42.65% score, it’s that Harbor finally removes the noise. No more wrestling with container orchestration, no more hand-rolling verification scripts for every new task. You get a clean, repeatable signal: did the agent complete the job or not?
That binary reward, across hundreds of diverse terminal interactions, turns vague intuition into hard data. Some tasks take ten minutes and a hundred tool calls; others zip by in seconds. Harbor handles both, silently.
The framework doesn’t just run agents, it forces them to prove themselves in environments that mirror real-world complexity, from git merge conflicts to SQLite instrumentation. And because it ships with pre-built datasets like Terminal Bench, you can start measuring progress today, not next month. The sandbox wraps file operations, shell commands, and automated scoring into one lean interface.
That means more time improving your agent, less time debugging infrastructure. The baseline results from DeepAgents CLI show there’s plenty of room to grow. Harbor gives you the tools to chase that growth, systematically, without ever losing sight of what matters: performance that actually transfers from the bench to the terminal.
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Common Questions Answered
How does Harbor simplify AI agent testing across different cloud platforms?
Harbor provides a standardized framework for evaluating AI agents in containerized environments across multiple sandbox providers like Docker, Modal, Daytona, E2B, and Runloop. The framework automates test execution, handles reward scoring, and manages complex infrastructure challenges, allowing researchers to focus on agent performance assessment.
What key features does Harbor offer for AI agent evaluation?
Harbor supports automatic test execution on benchmark tasks and provides automated reward scoring to verify task completion. It also includes a registry of pre-built evaluation datasets like Terminal Bench, enabling researchers to systematically assess AI agent capabilities in isolated, controlled environments.
Why is Harbor considered a significant advancement in AI research infrastructure?
Harbor tackles one of the most challenging problems in current AI development by offering a comprehensive solution for consistently and rigorously testing agent capabilities across diverse cloud platforms. Its ability to handle infrastructure complexity while providing standardized, sandboxed execution makes it a powerful tool for AI researchers and developers.
Further Reading
- Papers with Code Benchmarks — Papers with Code
- Chatbot Arena Leaderboard — LMSYS
