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A safer AI agent stack separates the model’s instructions from the capabilities it can actually use, then limits where those capabilities run and what they can reach. The model proposes actions; the orchestration harness routes them; tools and the execution environment determine what can happen. Use narrow permissions, isolated execution, restricted network access, and approval checks for consequential actions rather than relying on prompts to keep an agent within bounds.
What belongs in an AI agent stack?
An AI agent is a system of cooperating parts, not just a model with a prompt. OpenAI’s Agents API documentation describes an agent in terms of its model, instructions, tools, and available MCP servers, with an optional environment for files and commands. The orchestration harness and the environment matter because they govern how actions are run and what they can access.
Model
The model interprets the task and context, then produces a response or proposes an action such as calling a tool. It does not, by itself, define the system’s security boundary: that depends on what the surrounding application permits and where execution happens.
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Instructions and skills
Instructions tell the agent how to approach a task. A skill is reusable guidance or a procedure the agent can consult. They can improve consistency, but they are not access controls: telling an agent not to read a file does not technically prevent it from doing so if its runtime can access that file.
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Tools and integrations
Tools expose actions or data, such as searching a service, editing a record, or running a function. An MCP server—MCP means Model Context Protocol—can provide tools or context through that protocol. A tool’s practical authority depends on its implementation and the credentials or permissions behind it; a tool that can write data is materially different from one that can only read it.
Harness or orchestrator
The harness runs the agent loop: it sends context to the model, routes proposed tool calls, handles results and handoffs, and may manage approvals, state, tracing, or recovery. OpenAI’s sandbox guidance describes this as the control plane, distinct from the compute where task-specific work runs.
Execution environment
The environment is the workspace in which commands or agent-generated code run. It may define available files, packages, network access, and credentials. A sandbox is useful when work needs a filesystem, commands, artifacts, or resumable state; a short interaction that needs none of those may not need a persistent workspace.
Permissions and policy
Policies decide which actions may proceed automatically, which pause for a person, and which are evaluated by application logic. They operate alongside workspace restrictions and the permissions granted by connected services; no single approval setting necessarily overrides the others.
How does an agent action move through the stack?
- The user gives a task. The harness supplies the model with relevant instructions, context, and the tools available for that run.
- The model proposes a response or tool call. Instructions can shape this choice, but they do not replace technical enforcement.
- The harness checks and routes the proposal. Depending on policy, it can allow the call, request approval, evaluate it in application logic, or reject it.
- The tool or environment performs the action. The action is limited by the tool’s own authorization and by the files, network, and other resources available where it runs.
- The result returns to the model. The model can use the result to continue the task; the application can then review, record, or present the resulting output.
In shorthand: user task → harness and model → proposed tool call → policy and authorization checks → tool or sandbox → result → reviewed output or action. A prompt instruction is only one layer in this chain; runtime and provider-side controls determine what is technically possible.
Where should the agent loop and execution run?
There is no universally safest architecture. The choice changes who controls orchestration and state, where tools execute, and how much integration work the application must do. OpenAI’s documentation describes three approaches for its own platform; these distinctions should not be treated as a ranking of all agent platforms.
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| Approach | Who controls the orchestration? | What the documentation says it suits | Main trade-off |
|---|---|---|---|
| Managed Agents API | The service manages more of the agent workflow and progress. | Long-running tasks that benefit from managed progress. | Less direct control over orchestration than an application-run loop. |
| Agents SDK | The application uses the SDK to run custom tools and workflows. | Applications that need their own tools and workflow logic. | The application takes on responsibility for more of the agent loop and its controls. |
| Direct Responses API | The application integrates more of the surrounding workflow itself. | Teams that want the most control over the integration. | More integration work is required. |
These descriptions reflect OpenAI’s Agents documentation, accessed October 7, 2026. They do not establish how another provider’s managed service, SDK, or API behaves. Compare implementations by asking who controls state and handoffs, where tools run, who operates the sandbox, how network and files are restricted, how credentials reach tools, and which approval and provider rules still apply.
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How do you give an agent tool access safely?
Start with the smallest useful capability set
- Enable only tools needed for the task, and prefer narrow operations over broad, general-purpose access.
- Separate read actions from write, delete, publish, or financial actions where the integration allows it.
- Use service-side authorization that limits the agent to the relevant account, records, or workspace. A tool being available to the model does not make its provider permissions safe by default.
- Review tool definitions and their effects before enabling them. Recheck them when a tool or connected server changes.
Keep secrets out of agent-visible compute
OpenAI’s official API sandbox security documentation states: “Agent-generated code can access the files, credentials, and network available to its environment.” Keep application API keys outside that environment. Where possible, have a trusted application handler or proxy perform the authenticated operation and return only the data the agent needs. Injecting a stored secret into an environment still makes it available to code running there.
Isolate execution and limit network access
Use an isolated environment for code and commands, and restrict outbound connections to approved destinations when the task needs network access. The right network boundary depends on where a tool connection originates: from the customer’s environment or from a remote service. A sandbox narrows exposure, but it does not by itself remove access to network destinations or credentials that are made available inside it.
Assess connected tool servers
A connected server is part of the agent’s attack surface, not merely a convenient extension. OpenAI’s ChatGPT developer-mode and MCP guidance warns that unsafe or untrusted MCP servers can increase security exposure, including prompt-injection exposure. Verify the server and the actions it offers before enabling it, and review changes to its tool definitions rather than assuming a previously checked integration remains unchanged.
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Use automatic execution for bounded, low-impact operations that have already been constrained by the application. Pause for human review before consequential or difficult-to-reverse actions, such as deleting important data, sending a message externally, publishing content, or making a purchase. An approval step is a decision point, not a substitute for narrow tool permissions.
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Permission systems can use different policies. Anthropic’s permission-policy documentation, observed with the version marker managed-agents-2026-04-01 and accessed October 7, 2026, describes policies that allow actions, pause for approval, or have a server evaluate them. Custom tools executed by an application are governed by that application. These are provider-specific examples, not universal labels or behavior.
OpenAI’s ChatGPT app-permission guidance also distinguishes an approval from other restrictions: a saved approval does not override workspace rules, provider permissions, or safety protections. Treat each as a separate gate, and provide a way to revoke access when it is no longer needed.
What should you check before deploying an agent?
- Capability: Can you explain what each enabled tool reads or changes, and remove anything unnecessary?
- Identity: Are provider credentials scoped to the minimum required account and operations, with application secrets kept outside the agent’s execution environment?
- Execution: Are files, commands, packages, and network access limited to what the task requires?
- Approval: Are high-impact actions gated, and can the application reject a call even when the model requests it?
- Third parties: Have connected tool servers and their current actions been checked, including after changes?
- Operations: Can the application inspect what happened, recover from failures, and revoke access without relying on the model to cooperate?
The best boundary is enforced where the action happens: by the application, runtime, tool implementation, or service authorization. Instructions and skills can guide the agent inside that boundary, but should not be the only thing standing between a model-generated action and sensitive data or irreversible changes.
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