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Random freezes, missing sound and display glitches usually trace back to one bad driver. Find and replace yours safely.Free scan · under a minuteA safer AI agent stack separates what the model is asked to do from what it is technically allowed to do. The model interprets the task; instructions and skills guide it; tools provide capabilities; and the orchestration harness, execution environment and permission checks determine how those capabilities run. Do not rely on prompt instructions alone to protect files, credentials or connected services.
What makes up an AI agent stack?
An agent is a system, not just a model. OpenAI’s Agents API documentation describes an agent in terms of its model, instructions, tools and any connected MCP servers; an optional environment can provide a workspace for files, skills and commands. The harness that runs the agent loop and the policies that constrain actions matter just as much to safety.
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- Model: interprets the task and available context, then proposes a response or action. It does not, by itself, define the security boundary.
- Instructions and skills: explain goals, constraints and reusable procedures. They can shape behavior, but they do not technically remove access to a tool or resource.
- Tools and integrations: expose functions, hosted capabilities or connected services. Depending on their design and authorization, they may read data or make changes.
- Harness or orchestrator: runs the agent loop, routes tool calls, manages state and handoffs, and can support approvals, tracing and recovery. OpenAI’s sandbox guidance calls this the control plane.
- Execution environment: supplies the filesystem, commands, packages and network available during task execution. It may be a sandbox or another workspace.
- Permissions and policy: decide which actions may run automatically, which need approval, and which are evaluated by an application or server. Workspace rules and the authorization of connected providers also apply.
These layers answer different questions. A skill might tell an agent how to prepare a report; a file-reading tool makes files available; the environment determines which files it can reach; and policy determines whether a consequential action proceeds without review.
How does a tool call move through the stack?
A typical cycle is: user task → model and harness → proposed tool call → policy and authorization checks → tool or sandbox → result returned to the model → reviewed output or action. Depending on the implementation, the harness may run tools itself, send a request to a connected service, or delegate execution to a separate environment.
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This distinction is important because instructions are behavioral guidance, not enforcement. A request such as “do not open private files” cannot substitute for restricting the environment to the files the task actually needs. OpenAI’s official API sandbox security documentation states: “Agent-generated code can access the files, credentials, and network available to its environment.”
Where should the agent loop and execution run?
Choose the runtime based on how much control you need over orchestration, state and execution—not on a generic claim that one architecture is always safest. OpenAI’s documentation describes three API approaches; the comparison below reflects that vendor’s descriptions, not a universal ranking of agent platforms.
| Approach | What the documentation says it suits | Control and trade-off |
|---|---|---|
| Managed Agents API | Long-running tasks with managed progress | OpenAI manages more of the agent workflow; inspect the available controls and execution boundary for the particular use case. |
| Agents SDK | Custom tools and workflows inside an application | The application can define more of the workflow and tool behavior, with corresponding implementation and operational responsibility. |
| Direct Responses API | Applications that need the most control over the interaction | Offers more direct control, but requires more integration work to provide orchestration and related behavior. |
OpenAI’s comparison of these approaches is in its “Agents | OpenAI API” documentation. The available amount of control does not alone establish how isolated a particular deployment is; check where each tool executes, what state persists, and which permissions govern it.
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Separate control plane from compute when practical
Keep trusted orchestration functions—such as authentication, billing, audit logging, human review and recovery—in application-side infrastructure where feasible. Run task-specific file and command work in a separate, restricted environment. OpenAI’s “Sandbox Agents | OpenAI API” guidance describes the harness as the control plane and distinguishes it from compute. Putting the harness and model-directed execution in the same compute boundary can be convenient for a prototype, but combines orchestration and execution exposure.
When should an agent use a sandbox?
A sandbox is useful when a task needs a workspace, command execution, artifact creation or filesystem state that can be resumed. A short, stateless response may not need a persistent execution environment. The decision is about the task’s required capabilities: do not give an agent a workspace merely because the system is called an agent.
Isolation is only one part of the boundary. OpenAI recommends isolated compute and restricting outbound network access to approved endpoints. The appropriate network rule depends in part on where a tool connection originates—for example, from the customer’s environment or from a remote service. A sandbox with broad network access can still reach external destinations, and a restricted filesystem does not by itself constrain what an authorized connected service can do.
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How do you limit an agent’s permissions?
Grant the smallest useful set of capabilities, and place review at actions with meaningful consequences. Anthropic’s permission-policy documentation describes policies that can allow tool calls, pause for approval or have a server evaluate them; custom tools executed by an application are governed by that application. The documentation version marker observed was managed-agents-2026-04-01 (accessed 2026-10-07); policy names and availability can differ across products and versions.
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- Require approval before actions that could publish, send, delete, purchase or otherwise create a consequential external effect.
- Use server-side evaluation when an application needs to check context or enforce a rule before allowing a tool call.
- Keep provider authorization narrow. A tool’s access is also limited—or enabled—by the connected service’s permissions.
- Revoke access when it is no longer needed. Workspace controls, provider permissions and agent-level approvals are distinct controls; changing one does not necessarily change the others.
OpenAI’s Help Center guidance on managing app permissions in ChatGPT notes that saved approvals do not override workspace restrictions, provider permissions or safety protections. Treat approval as one decision point in a larger authorization chain, not as a universal grant of authority.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How should you handle credentials and network access?
Keep application secrets out of agent-visible environments. OpenAI’s sandbox security guidance warns that agent-generated code can access the files, credentials and network available to its environment. If a stored secret is injected into that environment, code running there may be able to read it.
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Where possible, keep credentials in trusted application infrastructure and broker third-party operations through a narrow proxy or application-side handler. Give the agent a limited operation rather than a reusable secret when the integration permits it. Restrict outbound connections to destinations the task requires, and decide where to enforce that restriction based on where the connection originates.
What should you check before enabling an MCP server?
A connected tool server is part of the attack surface, not a neutral extension of the model. OpenAI’s Help Center article “Developer mode and MCP apps in ChatGPT” cautions that unsafe or untrusted MCP servers can increase security exposure, including prompt-injection risk.
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- Verify who operates the server and whether it is trusted for the data and actions involved.
- Review the actions and permissions it exposes before enabling it.
- Recheck tool definitions when they change; a previously reviewed integration may expose new behavior after an update.
- Limit the data and provider permissions available to the connection to what the task requires.
- Require human review for consequential actions rather than assuming that a trusted server makes every call safe.
A practical design checklist
- Define the task and its boundaries. Identify required data, actions, outputs and actions that must remain off-limits.
- Select the runtime. Decide who controls the loop and state, where tools execute, and whether the task needs a sandbox or persistent workspace.
- Separate guidance from capability. Write instructions and skills for behavior, then independently configure the available tools and environment.
- Constrain execution. Limit files, commands and outbound network access to the task’s needs.
- Protect credentials. Keep reusable secrets out of the execution environment; use application-side handling or a restricted broker where feasible.
- Set action policy. Choose which calls can run automatically, which require approval, and which need server-side evaluation.
- Review integrations. Verify connected servers and their actions, and reassess them when their definitions or permissions change.
- Plan for oversight and recovery. Keep appropriate logs and a way to review, stop or recover from actions through trusted infrastructure.
The central design choice is not which model sounds most capable; it is how narrowly the whole system limits what that model can cause. Build the boundary into the runtime, tool authorization and application policy, then use instructions to guide behavior within it.
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