The Tool Desk
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What “control” means when an assistant can act
An AI assistant that can use connected tools may read information, change records, send messages, or operate software. Its instructions alone do not determine what it can affect: available tools, account permissions, execution environment, and approval rules all matter. Treat setup as a set of independent controls rather than a single “safe mode.”
For any task, first define the intended outcome, the accounts and data required, and actions that are out of scope. That boundary gives you a practical test for every permission you enable.
Set up access in layers
Tool access is not one switch. In ChatGPT workspaces, for example, app availability, who can use an app, the actions it can perform, and when it must ask are distinct controls. Provider authorization and OAuth scopes are separate again. OpenAI explains the distinctions in its guidance on admin controls for apps.
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For connected apps, permission choices vary by account, app, connected account, and workspace. Options may include asking before reading or making changes, allowing reads while asking before changes, or allowing some low-risk actions while requiring review for higher-risk ones. A setting that allows supported actions without additional prompts carries elevated risk and is not offered by the standard account-wide or workspace-wide selectors. See Managing app permissions in ChatGPT for current details.
These controls do not substitute for one another: changing an app permission does not connect an account, grant provider permissions, change access in the source service, or override workspace policy. In managed workspaces, administrators may deny sensitive actions instead of presenting an approval prompt. Also, changing an app permission does not disconnect it or revoke provider access already granted; disconnect the app or unlink provider access if you need to stop future access.
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Use this setup sequence
- Define the task boundary. Write down the desired result, the data and accounts needed, and prohibited actions. Keep the scope narrow enough that you can check permissions against it.
- Inventory tools and separate read from write. List every connected app or custom tool. Enable only the actions the task needs, and distinguish reading from creating, editing, deleting, sending, or sharing wherever the product allows.
- Assign approval by consequence. Allow routine, low-risk steps only when they are easy to inspect or reverse. Require review for actions such as sending a message, inviting someone, changing or deleting a record, altering access or security settings, making a financial transaction, or sharing sensitive personal, financial, health, or identity data when the product supports those distinctions. These are examples from OpenAI’s app-permission guidance, not a universal risk taxonomy.
- Preview long plans before execution. Review the assistant’s proposed sequence before it starts, then retain separate approval gates for sensitive steps. Anthropic describes Claude Code Plan Mode as a way to review and edit a plan before it runs; its guidance also notes that repeated prompts can become friction and may be tuned out. Product behavior is specific to the product and configuration.
- Apply boundaries outside the assistant’s instructions. Restrict what the environment can access, and add checks at the point each consequential action executes.
- Protect against untrusted content. Treat web pages, documents, messages, and retrieved data as data rather than instructions. Limit what each tool receives and what information can pass between workflow steps.
- Record actions and revisit access. Keep a usable trail of requests, tool calls, approvals or denials, results, and relevant boundary decisions. Recheck permissions when a task, connected account, or tool’s available actions change.
For custom agents, enforce policy at the tool boundary
If you build an agent with the OpenAI API or Agents SDK, do not assume the application automatically inherits Codex Auto-review or that model-level checks surround every action. OpenAI’s documentation says: “If you need checks around every custom tool call in a manager-style workflow, don’t rely only on agent-level input or output guardrails.” Put a policy check next to each custom tool that can cause a side effect.
Before execution, validate the proposed target, action, arguments, calling identity, and whether the request falls within the approved task scope and time window. Deny disallowed calls. Pause ambiguous or high-risk calls for explicit human approval. If approval is delayed, preserve the pending run and resume that same run after the decision rather than starting a fresh sequence. The process is described in OpenAI’s guardrails and human review guidance.
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Pair approvals with technical limits
An approval rule and a sandbox do different jobs. Approval determines when the assistant must stop and ask; a sandbox limits where it can operate—for example, which paths it can write to, whether it can use the network, and what remains protected. Neither replaces the other. OpenAI describes this approach in Running Codex safely at OpenAI.
For sensitive workflows, set independent limits for filesystem, network, identity, and project access. Configure the workflow to fail closed for actions requiring review if the policy reviewer times out or is unavailable. The exact controls depend on the platform and execution environment.
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Reduce prompt-injection and data-exposure risk
Prompt injection is untrusted text that tries to override instructions and may induce data exposure or misaligned actions. A page or document the assistant reads should not become a higher-priority instruction merely because it appears in the workflow. OpenAI’s agent safety guidance advises against placing untrusted variables in developer messages and recommends structured outputs to constrain information flow between steps.
Send each connected tool only the information it needs. Even with guardrails, a model may send more data to a connected MCP than a user expected. Anthropic likewise recommends careful choices about tools, data, permissions, and operating environments, using layered defenses rather than relying on one safeguard. Its discussion is available in Trustworthy agents in practice.
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Keep an audit trail that helps you intervene
Record enough to reconstruct what the assistant attempted and what happened: the request, relevant tool arguments, approval or denial, execution result, and applicable policy or network decisions. Where available, include prompts and tool activity. OpenAI reports that Codex supports OpenTelemetry export for events including prompts, tool approval decisions, tool results, MCP usage, and network allow-or-deny events in its Codex safety overview.
A plan preview is useful for supervising a long workflow, but it does not replace step-level approval for sensitive actions. Review permissions whenever the task, account, or available actions change; if your goal is to end access, revoke or disconnect at the provider level as needed.
What varies by platform
- ChatGPT connected apps: Permission options and available actions depend on the account, app, connected account, and workspace. Workspace administrators can impose controls; some sensitive actions may be denied rather than offered for approval. See app permissions.
- ChatGPT workspaces: Role access, enabled actions, and permission behavior are separate. “Disable new actions” applies to actions introduced later, not actions already enabled. Provider approval, OAuth scopes, and ChatGPT action settings are separate checks. See workspace admin controls.
- Custom OpenAI API or Agents SDK workflows: Build review and enforcement into your application, especially at tools that cause side effects; do not assume a separate product’s review defaults apply. See guardrails and approvals.
- Claude products: Anthropic describes configurable tool permissions, read-only defaults in some Claude Code examples, and plan review before execution. These are product-specific examples, not guarantees shared by all assistants. See trustworthy agents and Anthropic’s framework.
Standards work is also evolving: NIST announced its AI Agent Standards Initiative on February 17, 2026, covering industry-led standards, open protocols, and research in agent security and identity. NIST described it as an ongoing initiative, with further research, guidelines, and deliverables to follow—not as a completed standard. See the NIST announcement.
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