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Give an AI agent only the tools, files, network access, identities, and credentials its task requires. Enforce those limits with a sandbox, then use approval gates for sensitive actions that still need human or policy review. Sandboxing and approval are complementary: one limits what the agent can reach; the other decides whether a particular action may proceed.
What least privilege means for an AI agent
Least privilege is a design rule: give an agent only the access needed for its assigned task, keep each scope narrow, and remove access it does not need. For a coding agent, that means checking more than its list of tools. Consider every resource available through those tools and through the environment in which generated code runs.
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- Tools: Enable only the tools needed for the task, and distinguish tools the agent can call directly from custom tools your application executes.
- Files: Limit which paths the agent can read or change. A task that edits one project should not automatically grant access to unrelated projects or user data.
- Network: Restrict outbound connections to approved destinations. Account for the network path of each integration: a locally executed MCP connection and a remote service may connect from different places.
- Credentials: Give the agent only scoped credentials for approved services. Keep application keys out of the execution environment.
- Identity and project: Run the agent under an identity and within a project or environment whose permissions match the task—not a broadly privileged account by default.
OpenAI’s Sandbox security documentation recommends isolated compute, separating environments when data should not be shared, and restricting outbound access to approved endpoints.
How sandboxing and approval gates differ
| Control | What it does | What it cannot do alone |
|---|---|---|
| Least privilege | Sets the design goal: limit access to what the task requires. | It is not itself an enforcement mechanism; it must be implemented in tools, runtime, identity, and application policy. |
| Sandbox | Enforces a technical boundary around execution, such as writable paths, network access, or available resources. | It cannot decide whether a specific permitted action is appropriate for the user’s request. |
| Approval gate | Pauses, allows, or rejects a selected action based on human review or policy. | It does not contain the agent or protect resources that remain reachable between prompts. |
OpenAI’s API documentation puts the risk plainly: “Agent-generated code can access the files, credentials, and network available to its environment.” A sandbox reduces what is available to that code. An approval rule can then require review when an action crosses a boundary or has a meaningful side effect. OpenAI describes sandboxing as the technical boundary and approval policy as deciding when review is needed in Running Codex safely at OpenAI; its Guardrails and human review guide distinguishes automatic guardrails from human review.
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Keep secrets out of the agent’s execution environment
A secret manager or vault does not protect a credential after an application injects it into an environment the agent or its generated code can read. In that case, the credential is available to the code whether or not the model was explicitly shown its value.
Keep application API keys outside the executor environment. When an agent needs to access a third-party service, use a proxy or application layer to provide narrowly scoped credentials for approved hosts; the sandbox can use a placeholder instead of holding the real secret. This limits what a compromised or misbehaving process can expose. OpenAI’s sandbox guidance covers both executor isolation and this credential-separation approach.
Design approval gates around side effects
Put review at the point where an action can change something, disclose data, spend money, or affect another system—not merely at the point where the agent proposes a plan. The reviewer or policy should be able to evaluate the proposed target, action, arguments, identity, and scope before the tool executes.
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Actions that merit review
Set gates according to the consequences and reversibility of the action. A useful policy distinguishes routine, bounded work from actions with wider or lasting effects.
- Writing outside the task’s designated workspace or changing protected files.
- Sending data to an unapproved destination or using a credential with broader scope than the task requires.
- Publishing, deploying, deleting, or modifying shared or production resources.
- Actions whose target or arguments are ambiguous, or whose consequences are difficult to reverse.
For sensitive actions, pause when the reviewer cannot see enough context to make a sound decision. Record the decision and result. In a cybersecurity workflow where review is required, fail closed if the review mechanism is unavailable rather than silently allowing the action.
Avoid approval fatigue
Prompts work only if people can assess them and respond thoughtfully. Anthropic reports that users approved roughly 93% of Claude Code permission prompts in its telemetry, and warns that repeated prompts can reduce attention. That figure describes Claude Code users in Anthropic’s own telemetry; it is not an independent measurement across AI agents. Avoid asking people to approve every low-risk operation. Let narrow, sandboxed routine actions proceed under policy, and reserve interruptions for decisions that materially change risk.
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Enforce policy on every tool path
A gate is effective only if the action actually passes through it. Do not assume that an agent-level guardrail automatically protects every custom tool call.
OpenAI says its agent-level input and output guardrails do not run around every custom tool call. Tool guardrails apply to attached function tools, but application-defined tools need their own enforcement. Likewise, Anthropic says Managed Agents permission policies do not govern custom tools executed by the application. Put authorization, argument validation, and any required approval in the application’s custom-tool path before it causes a side effect. See OpenAI’s guardrails guidance and Anthropic’s Managed Agents permission policies.
Compare policy options by where and how they enforce
When choosing a runtime or designing your own agent harness, compare the enforcement behavior—not just the names of its modes.
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| Question | Why it matters |
|---|---|
| Can policy allow, ask, or deny? Can a reviewer override a denial? | Clarifies the available outcomes and whether a denial is a hard boundary. |
| Where is policy enforced: runtime, server, application, or reviewer? | Identifies which layer must be trusted and maintained. |
| Are filesystem, network, credentials, identity, project, and tool availability scoped separately? | Reveals whether one broad permission is standing in for several distinct controls. |
| Does review show the exact target, action, arguments, and scope? | Without this context, a person may be unable to judge the proposed action. |
| Are application-defined tools governed too? | Custom tools may bypass platform-level policies unless the application enforces them. |
| Are approvals, denials, results, and network decisions logged? | Audit records help explain what the agent attempted and what policy allowed. |
| What happens if review is unavailable? | Fail-open and fail-closed behavior produce different risks. |
One platform-specific example: Anthropic Managed Agents
Anthropic’s Managed Agents permission-policy documentation describes three outcomes: always_allow runs enabled tools automatically; always_ask pauses for approval; and auto evaluates a call and can allow, deny, or pause. The page states that agent toolsets default to always_allow and MCP toolsets to always_ask. These are platform-specific defaults, not general recommendations; check the current documentation and API version before relying on them. The documentation labels the feature beta and displays API version managed-agents-2026-04-01. Disabling a tool removes it from the agent, while permission policy determines when an enabled tool may run. Application-executed custom tools remain outside these policies. Details are in Anthropic’s Permission policies documentation.
Building with OpenAI’s API
OpenAI’s API guidance says Responses API and Agents SDK applications do not automatically inherit Codex Auto-review. Teams building their own harness need to implement review and enforcement in their application where required. A product’s approval feature should not be assumed to cover a separate API integration.
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Maintain agent-aware logs that connect the user request to tool activity and outcomes. OpenAI describes logging user requests, tool activity, approval decisions, results, and relevant network decisions or blocks, with logs suitable for centralization in SIEM and compliance systems. These records make it possible to investigate whether an agent exceeded its intended scope and whether a gate worked as designed.
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Before relying on a permission design, verify the boundary and every route around it:
- Confirm which files the agent can read and write, and whether unrelated data is isolated.
- Check that outbound connections are limited to approved endpoints, including connections made by integrations.
- Verify that real application credentials are not readable from the executor and that brokered credentials are scoped appropriately.
- Exercise each side-effecting tool path, including custom application tools, to confirm required checks happen before execution.
- Confirm the reviewer receives actionable context and that policy outcomes, tool results, and network blocks are recorded.
- Determine whether unavailable approval blocks the action or allows it to proceed.
For background on combining execution boundaries with supervision, Anthropic explains its approach in How we contain Claude across products.
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