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
AI research just got a serious upgrade in testing and evaluation. Developers wrestling with agent performance across different cloud platforms now have a powerful new tool at their disposal.
The Harbor framework emerges as a critical solution for systematically assessing artificial intelligence agents in complex, distributed computing environments. Its approach tackles one of the most challenging problems in current AI development: consistently and rigorously testing agent capabilities across multiple sandbox platforms.
By supporting execution environments like Docker, Modal, and Daytona, Harbor offers researchers an unusual level of standardization. This means teams can now compare agent performance more precisely, without getting bogged down in technical infrastructure challenges.
The framework promises to simplify what has traditionally been a fragmented and time-consuming process of benchmarking AI agents. Researchers can now focus on the agents themselves, rather than spending countless hours managing different testing setups.
With computational resources becoming increasingly critical in AI development, Harbor represents a significant step toward more efficient, reproducible research methodologies.
Harbor: Sandboxed Agent Execution This is where Harbor comes in. Harbor is a framework for evaluating agents in containerized environments at scale, supporting Docker, Modal, Daytona, E2B, and Runloop as sandbox providers. It handles: - Automatic test execution on benchmark tasks - Automated reward scoring to verify task completion - Registry of pre-built evaluation datasets like Terminal Bench Harbor handles all the infrastructure complexity of running agents in isolated environments, letting you focus on improving your agent.
Harbor offers a sandbox environment with shell-execution capabilities. We built a HarborSandbox backend that wraps this environment and implements file-system tools (e.g., edit_file , read_file , write_file , ls ) on top of shell commands. It measures how well agents operate in computer environments via the terminal.
Example tasks: path-tracing : Reverse-engineer C program from rendered imagechess-best-move : Find optimal move using chess enginegit-multibranch : Complex git operations with merge conflictssqlite-with-gcov : Build SQLite with code coverage, analyze reports Tasks have a wide range of difficulty--some require many actions (e.g., cobol-modernization taking close to 10 minutes with 100+ tool calls) while simpler tasks complete in seconds. Automated Verification: Each task includes verification logic that Harbor runs automatically, assigning a reward score (0 for incorrect, 1 for correct) based on whether the agent's solution meets the task requirements. Baseline Results We ran the DeepAgents CLI with claude-sonnet-4-5 on Terminal Bench 2.0 across 2 trials, achieving scores of 44.9% and 40.4% (mean: 42.65%).
Harbor's emergence signals a significant step for AI agent testing infrastructure. The framework simplifies complex evaluation processes by providing standardized, sandboxed execution across multiple cloud platforms.
Researchers now have a powerful tool for rigorously assessing AI agent performance. By supporting diverse sandbox providers like Docker, Modal, and Daytona, Harbor offers unusual flexibility in testing environments.
The framework's key strengths lie in its automated capabilities. It handles test execution, reward scoring, and manages pre-built evaluation datasets like Terminal Bench with remarkable efficiency.
What sets Harbor apart is its ability to abstract away infrastructure complexities. Developers can focus on agent performance rather than getting bogged down in technical setup and isolation challenges.
Still, questions remain about the depth and breadth of its evaluation mechanisms. How full are its benchmark tasks? What nuanced performance metrics can it truly capture?
For now, Harbor represents a promising approach to standardizing AI agent testing. Its multi-platform support and automated features suggest a more simplified future for AI research and development.
Further Reading
- Top AI Code Sandbox Products in 2025 - Modal - Modal Blog
- Docker Brings Agentic Apps to Life with New Compose Support, Cloud Offload, and Partner Integrations - Docker Newsroom
- Docker Extends AI Momentum with MCP Tools Built for Developers - Docker Newsroom
- Top 5 Docker Containers Transforming LLM Development in 2026 - US Data Science Insights
- Daytona Compute 2026 - Daytona
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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.