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How the instruction, tool, and permission layers fit together
Think of an agent’s operating setup as three connected layers. All three matter: good written guidance cannot make an overly broad environment safe, and a tightly restricted environment cannot tell an agent which project conventions to follow.
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| Layer | What it does | What it does not do on its own |
|---|---|---|
| Instructions | Provide the task, project context, and behavioral guidance, including repository-local conventions. | They do not enforce filesystem, network, or identity restrictions. |
| Tools | Expose capabilities such as shell commands, filesystem operations, APIs, and integrations. | They do not necessarily limit what the execution environment can access through those capabilities. |
| Runtime and permissions | Set practical access through the execution environment, mounts, credentials, network policy, and approval controls. | They do not replace clear task guidance or careful tool design. |
How repository instructions guide an agent
An agent may receive instructions through its configuration and through files in the workspace. OpenAI’s agent configuration guide describes instructions as part of agent behavior. Its sandbox guide describes workspace files such as AGENTS.md as a place for longer task specifications and repository-local guidance.
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That guidance can explain how to build or test a project, which conventions to follow, and what the task is meant to accomplish. It remains behavioral context, not an access-control mechanism: an instruction such as “do not read secrets” does not prevent code from reading a secret that is accessible in the environment.
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Repository instruction filenames and precedence rules are not established here as a universal convention across coding-agent products. Check the documentation for the specific agent rather than assuming every product discovers or prioritizes instruction files in the same way.
How configured tools shape what the agent can attempt
Tools are the capabilities an application makes available to an agent. Depending on the setup, these might include shell commands, filesystem operations, APIs, or MCP integrations. OpenAI’s agent configuration guide describes configuring tools on an agent; its tools guide explains that a model generally selects among enabled tools in response to a prompt, while an application can guide tool selection with tool-choice settings.
A useful design rule is to expose only the operations the task needs. If an agent only needs to read a repository and run a test suite, it may not need a broad deployment API or unrestricted access to unrelated files. Tool exposure and runtime restrictions should complement each other: a narrow toolset reduces available actions, while the environment limits the reach of the actions that remain.
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What permissions and runtime boundaries actually control
Instructions and tools operate within an execution environment. That environment’s accessible files, credentials, and network determine what agent-directed code can reach. OpenAI states in its Sandbox security documentation: “Agent-generated code can access the files, credentials, and network available to its environment.”
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In practice, compare deployments by examining the specific boundaries below. OpenAI documents options including an OpenAI-hosted sandbox, a self-hosted sandbox, or no sandbox; these choices are not interchangeable, and actual controls depend on the implementation.
- Execution and operator: Identify where code runs and who operates that environment.
- Files and mounts: Check which repository files, mounted directories, and neighboring data are readable or writable.
- Network: Determine whether outbound access is disabled, unrestricted, or limited to approved hosts.
- Credentials: Find out how credentials are injected, scoped, and kept out of logs and source files.
- Available capabilities: Review the shell, filesystem, API, and MCP tools the agent can invoke.
- Approvals and records: Check which actions require approval and what traces or audit records capture tool calls and decisions.
How the harness and sandbox divide responsibilities
OpenAI’s sandbox guide distinguishes the harness—the control plane for the agent loop, tool routing, handoffs, approvals, tracing, recovery, and run state—from sandbox compute, where commands run and files, dependencies, storage, and artifacts reside. Keeping sensitive control-plane functions outside the execution environment can help isolate them, although implementations vary.
This distinction helps explain why a tool call, an approval decision, and the command’s execution are related but separate parts of a run. The harness can route and record a call; the compute environment determines what the command can reach; and approval controls can pause specified actions for human review.
How to combine restrictions with human approval
Approval is a review path for tool calls, not a replacement for restricting the environment. It is useful only when the proposed action is checked before it executes and the reviewer has enough information to judge its scope. A review screen that omits relevant arguments or targets makes meaningful assessment harder.
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OpenAI’s Sandbox security guidance recommends isolating workloads when their data must not be shared, limiting outbound access to approved endpoints where possible, and keeping application credentials outside the execution environment. For third-party access, scoped credentials and trusted proxies or function tools can reduce direct exposure. These controls work alongside approval rather than being replaced by it.
OpenAI’s May 8, 2026 safety article describes organizational goals that include keeping agents within technical boundaries, making higher-risk actions explicit, and preserving telemetry to audit behavior. Those goals should not be taken to mean every agent deployment has identical controls or outcomes.
A practical configuration checklist
- Write task and repository guidance: State what the agent should do and the project conventions it needs to follow in its configuration or workspace guidance.
- Expose the smallest useful toolset: Enable only the shell, filesystem, APIs, and integrations required for the task, and use application-level tool-choice controls where appropriate.
- Constrain the execution environment: Limit accessible files and mounts; isolate workloads whose data must remain separate.
- Constrain network and credentials: Allow only needed outbound endpoints where possible, and keep application keys outside the agent’s execution environment.
- Gate consequential actions: Require review before sensitive tool calls execute, and show reviewers enough detail to evaluate the proposed action.
- Keep useful records: Ensure the deployment provides appropriate traces or audit records for tool calls and approval decisions.
For products from different vendors, compare their documented execution environments, permissions, and data flows rather than assuming they share one repository-instruction convention. GitHub’s responsible-use guidance notes that agent products differ in those areas; implementation-specific claims should be checked against each product’s primary documentation.
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