An AI coding-agent score is meaningful only in the execution environment that produced it. The sandbox determines which files, tools, credentials, and network resources an agent can reach—so a score without those conditions is hard to interpret or reproduce.
What a sandbox controls in an agent evaluation
A sandbox is an isolated execution environment. It can include a filesystem, shell, installed packages, mounted data, exposed ports, snapshots, and controlled external access, depending on its configuration. OpenAI’s shell tool documentation describes these environment components.
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That boundary shapes both what an agent can do and what risks its actions create. As OpenAI’s sandbox security documentation puts it: “Agent-generated code can access the files, credentials, and network available to its environment.” OpenAI’s security guidance recommends isolated compute, restricted outbound access to approved endpoints, and keeping credentials separate from the execution environment.
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Describe the environment alongside the task. Without this information, readers cannot tell whether a result reflects the agent’s ability, the resources it could use, or a difference in setup.
#1 Best Overall
- Image and operating system: identify the OS or image and how it is initialized.
- Workspace contents and permissions: record the repository and mounted data, and whether the agent can read or change source files, hidden files, configuration, build scripts, and Git hooks. Docker notes that a mounted workspace may remain writable, including hidden files, configuration, build scripts, and hooks. Docker’s sandbox security documentation explains this boundary.
- Dependencies and tools: list installed packages, available commands, and any exposed ports.
- Network policy: specify whether outbound access is blocked or limited to approved endpoints.
- Credentials: describe whether credentials are available, and how they are kept apart from the agent’s execution environment.
- Reset procedure: explain how state is cleared or restored between runs, including whether snapshots are used.
Keep filesystem access, network access, and credentials distinct
These controls address different paths to data and services. Filesystem permissions limit what the agent can inspect or change locally. Network rules limit which external endpoints it can reach. Credential handling determines which secrets it can use if they are present or accessible.
Do not assume that restricting one makes the others safe. OpenAI advises against placing an application API key inside the execution environment and recommends restricting network access. Anthropic describes filesystem and network restrictions as complementary controls in Claude Code, with configurable allowed paths and domains. Anthropic’s security documentation outlines those controls.
Rank #2
Describe the isolation model without treating vendor claims as certification
“Sandbox” does not by itself specify a particular security boundary. For example, Docker describes its local sandboxes as microVMs with separate Linux kernels and identifies the hypervisor, network, Docker Engine, workspace, and credential proxy among its isolation layers. Docker’s sandbox documentation gives its design description.
Report the isolation model actually used in an evaluation. A provider’s architecture description is evidence about its stated design, not an independent comparative security certification.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Hold the environment steady when comparing agents
Use the same sandbox configuration when comparing systems, or clearly disclose every configuration change. If the environment changes between runs, the results do not isolate the agent as the cause of a score difference. The cited documentation establishes why environment details matter, but does not provide a controlled estimate of how a particular sandbox choice changes coding-agent benchmark scores. Do not assign a numerical score effect without a study that isolates that variable.
This also matters when reading benchmark results. OpenAI announced SWE-bench Verified as a human-validated subset intended to make evaluation of real-world software issue solving more reliable. Its announcement reported leaderboard scores as of August 5, 2024; those figures are historical, not current standings, and the announcement does not isolate sandbox configuration as an experimental variable. OpenAI’s announcement provides that context.
Quick Recap
A practical sandbox reporting checklist
- Define the task and starting state. Record the repository contents and any mounted data available to the agent.
- List environment capabilities. Name the OS or image, packages, shell and other tools, and exposed ports.
- Set and report filesystem permissions. State what paths are readable or writable, including hidden files, configuration, scripts, and hooks.
- Specify egress and secret handling separately. List allowed network access and explain whether credentials are absent, isolated, or brokered.
- Name the isolation model. Describe its boundary and layers without presenting a vendor description as independent validation.
- Explain initialization and reset. State how each run begins and how files, services, and other state are reset or restored.
- Keep conditions fixed or disclose deviations. Identify any changes between agents or runs, and avoid attributing score differences to the agent when the environment also changed.
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