The dependable way to keep a coding agent on task is to limit what it can actually access—not just to ask it to stay within scope. Define permitted files and actions, give the agent the narrowest useful workspace, and enforce the boundary with the harness or an operating-system sandbox. Then inspect the complete Git diff before accepting the work.
Define the task boundary before starting
Write down what the agent should change, which paths it may edit, which paths are off limits, and which actions require you to approve them. Keep the statement specific: for example, “Update the parser in src/parser and its tests; do not change dependencies, deployment files, or other packages; ask before running network commands.”
Start the agent in the smallest project directory that contains the files it needs. Keep unrelated repositories, credentials, and personal files outside its writable area. A prompt is useful for explaining intent, but it is not a technical access control: an agent may still make an unintended change if its tools and environment permit it.
Enforce the boundary with workspace and sandbox controls
Choose a mode that limits writes to the workspace or another explicitly selected root. Where available, use operating-system sandboxing to enforce that limit for commands and processes the agent launches, not just for edits made through its own file tools. Product behavior differs by tool, platform, and configuration, so verify the active settings rather than assuming a default applies to your setup.
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- Codex: OpenAI distinguishes sandbox restrictions from approval policy: the sandbox sets technical limits such as writable locations and network access, while approval settings govern when Codex asks before crossing a boundary. Its Windows engineering account describes a default that permits broad file reads, limits writes to the workspace, and blocks internet access unless requested; those details are specific to Codex on Windows as described by OpenAI. See OpenAI’s explanation of running Codex safely and its Windows sandbox engineering account.
- Claude Code: Anthropic describes sandboxing for the Bash tool that allows file access in the current working directory and blocks modification outside it. Claude Code on the web is described as using an isolated cloud sandbox and a proxy that checks Git interactions, including the configured branch. See Anthropic’s Claude Code sandboxing overview.
- Visual Studio Code: Built-in agent file access is workspace-limited, with optional read-only access to additional folders. VS Code also documents tool selection, temporary session permissions, worktrees, and change review. Its separate OS-level agent sandbox is documented as Preview on macOS, Linux, and WSL2 and Experimental on Windows; verify current status and support for your installation. See VS Code’s agent security documentation and its agent tools documentation.
- GitHub Copilot: Agent mode can choose files, edit them, and run commands. GitHub says users can review streamed edits and confirm or reject terminal commands unless automatic execution is configured. See GitHub’s Copilot agent-mode documentation.
These descriptions are not a universal ranking: writable roots, network behavior, approval prompts, and platform support vary. Check the product’s current documentation and the actual settings on the operating system and shell where the agent will run.
Restrict network access, tools, and approvals
Remove capabilities the task does not need. Disable network access when the agent can work offline, and turn off unneeded tools, integrations, or access to external systems. If it needs a particular capability, grant that capability deliberately rather than enabling every tool by default.
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Keep approval prompts for actions that would cross the allowed boundary. A setting that automatically approves every action can make prompts meaningless; VS Code documents an “Allow all” mode, and its security guidance warns that a Claude setting can bypass all permission checks. Use broad automatic approval only when the environment is separately isolated and that level of access is intentional.
For long-running tasks, deterministic hooks or checks can provide another guardrail where supported. Anthropic’s documentation recommends a Stop hook for auditable long-running Claude Code tasks. A hook can help check or record behavior, but it should complement—not replace—limits on what the agent can access.
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Separate the work, then inspect every change
A dedicated Git worktree or task branch makes it easier to isolate work and manage conflicts. It does not stop the agent from accessing files beyond that worktree if the harness or sandbox allows it; pair Git isolation with access controls.
- Create an isolated work area. Use a separate worktree or task branch if your tool and workflow support it, and verify that the agent still has only the intended permissions.
- Review the entire diff. Before committing, merging, or opening a pull request, inspect additions, modifications, renames, generated files, configuration changes, and deletions—not only the main source file.
- Check scope and behavior. Compare each changed path with the task boundary, then run the appropriate tests or other project checks.
- Undo unrelated edits before accepting the work. Revert changes that are outside scope, or discard the isolated work area and restart with tighter access if you cannot confidently separate intended changes from unintended ones.
GitHub says Copilot agent-mode edits can be reviewed and terminal commands confirmed or rejected unless automatic execution is configured. The exact review and command-approval experience therefore depends on the settings you use.
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What benchmark results do—and do not—show
The 2026 paper Overeager Coding Agents: Measuring Out-of-Scope Actions on Benign Tasks reports 500 validated scenarios and approximately 7,500 runs across four agent products—Claude Code, OpenHands, Codex CLI, and Gemini CLI—and six base models. In that tested setup, reported overeager rates ranged from 5.4% to 27.7% in a permissive cluster, compared with 0.2% to 4.5% under an ask-to-continue framework.
Those percentages describe the paper’s scenarios, products, and configuration. They are not a forecast of how likely a particular agent is to exceed scope in your project, nor a universal real-world incident rate. The practical implication is narrower: permission design can affect measured behavior, so evaluate the controls available in your own environment rather than relying on a general benchmark number.
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