The Tool Desk
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What should an AI agent be allowed to do?
Define permissions around the work the agent must perform, not around a broad label such as “assistant” or “admin.” Inventory each tool’s operations, data scope, side effects, destinations, credentials, and failure modes. Then allow only the operations and resources needed for the task.
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Classify individual actions by risk. A read operation can expose sensitive information; a write operation restricted to a small, low-impact resource may be less consequential than a broad deletion. Separate read and write capabilities where possible, restrict destinations and request rates, and use different tool sets or roles for tasks with different trust levels. OWASP recommends minimum necessary tools and per-tool scope controls in its AI Agent Security Cheat Sheet and Securing Agentic Applications Guide 1.0.
- Data access: Which records, files, accounts, or other resources may the agent read or change?
- Operations: Which methods are allowed—such as read, create, update, delete, send, or publish?
- Destinations: Where may data be sent, and which network destinations are permitted?
- Impact: Could the operation expose sensitive data, spend money, affect production, change privileges, or be difficult to undo?
- Execution: Does the tool run code, interpret content, or pass data to another service?
For example, a summarization agent may need to read approved messages but not send or delete them. A database assistant might need read-only queries against a defined dataset rather than unrestricted access to the database. Avoid wildcard permissions and arbitrary shell or code execution unless the execution is appropriately isolated.
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Where should permission checks happen?
Put a policy enforcement point between the model’s proposed call and the tool’s side effect. This can be application middleware, a service that brokers execution, or an API gateway. The essential property is that trusted code—not the model—makes the authorization decision independently for each invocation.
At execution time, the enforcement point should authenticate the agent, resolve the requested tool to an unambiguous identity, validate the arguments, and check whether that agent may perform that operation on that target. It should also apply relevant rate and egress limits before invoking the tool with the smallest usable credential. If the policy service, approval validation, or audit mechanism is unavailable during a high-impact operation, fail closed rather than proceeding without the check. OWASP summarizes the principle: “The agent can propose an action, but a policy service or execution component should independently validate scope, privilege, and approval state before execution.” See the OWASP AI Agent Security Cheat Sheet.
Permission to invoke a tool by name is not permission to use it against every resource. A policy check must evaluate the requested parameters and target as well as the tool identity. An agent allowed to update a particular record, for example, should not gain authority over all records merely because it can call the same update endpoint. An API or agent gateway can centralize authentication, authorization, per-agent or per-tool rate limits, and interaction logs; gateway placement alone does not make a policy sufficiently granular. OWASP discusses these controls in its Top 10 for Agentic Applications 2026.
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How do you put boundaries into practice?
- Inventory capabilities. Record each tool’s operations, resource scope, side effects, destinations, credentials, and failure modes. Decide which actions are sensitive or consequential rather than judging risk from the tool name alone.
- Create task-specific permissions. Give each agent or role only the tools it needs. Prefer read-only access for read-only tasks, constrain files to approved paths, APIs to specific resources and methods, and network access to approved destinations.
- Authenticate every caller. Give each agent instance or role a managed identity with a clear owner and de-provisioning path. Use a secrets manager, keep credentials out of model-visible context, and prefer short-lived credentials scoped to the task or session.
- Validate each requested action. At the execution boundary, check the caller, canonical tool identity, argument schema, operation, target, and applicable policy. Reject unknown tools, ambiguous resolution, invalid parameters, and out-of-scope targets.
- Apply limits before execution. Enforce rate and egress restrictions and invoke the tool with the least powerful credential that can complete the task. Do not rely on a prompt telling the agent to behave safely as a substitute for these checks.
- Require approval where impact warrants it. Define which operations need a person’s confirmation, and bind that confirmation to the exact proposed action.
- Record and review decisions. Log tool calls and authorization outcomes, monitor for unexpected patterns, and test the boundary after meaningful policy or tool changes.
For each invocation, an implementation can evaluate a policy tuple such as agent identity + tool + operation + target + normalized arguments. This is a design aid, not a universal policy language: the specific rules depend on the application’s resources and risks.
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Which actions need human approval?
Require approval when an action is destructive, financial, administrative, externally visible, or otherwise difficult to reverse. Examples include deleting data, making payments or transfers, publishing content, changing privileges, running bulk operations, and changing production systems. For eligible operations, show a clear plan or dry-run diff so the approver can understand the consequence before authorizing it.
Bind the approval to the actor, tool, target, normalized parameters, timestamp, and expiry. If any material part of the request changes, the old approval should no longer authorize it. Use short-lived authorization artifacts and replay protection, particularly for irreversible operations; apply stronger authentication where the consequences justify it. If the system cannot classify the action or verify its approval, fail closed. OWASP recommends approval and scope validation in the AI Agent Security Cheat Sheet and Securing Agentic Applications Guide 1.0.
A broad “approve this agent” setting is weaker than approval of a specific operation: it does not establish that the person reviewed the actual target and parameters the agent is about to use.
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Assume that external pages, documents, emails, and API responses can contain instructions intended to steer an agent. Delimiting untrusted content, validating inputs and outputs, and separating its processing can help, but none of those measures should grant or deny access. The execution boundary must still authorize every requested tool call.
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Limit the consequences of manipulated instructions by restricting egress, isolating tools or code that execute content, and requiring approval for high-impact actions. Pin and fully qualify tool identities so a name cannot resolve ambiguously. Monitor for suspicious cross-boundary sequences, such as reading sensitive information and then attempting to send it externally. OWASP covers tool scoping and prompt-injection-related risks in its AI Agent Security Cheat Sheet and Securing Agentic Applications Guide 1.0.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How do provider tool settings differ from authorization?
Provider settings can constrain which tools the model may select, but they are not a complete authorization system. OpenAI’s Chat API reference documents tool-choice modes including none, auto, and required, as well as an allowed_tools configuration that limits the available set. These are OpenAI API-specific controls; check the live Chat API reference for current behavior.
Even when the model is offered only an approved tool set, application-side checks are still needed to decide whether the authenticated agent may perform the exact operation on the requested resource with the supplied arguments. Selection limits reduce what the model can request; authorization determines what the application will execute.
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Middleware, a gateway, and provider-level settings can complement one another. Choose based on where the execution boundary can reliably see the caller, tool, target, and arguments—not on the label of the component. The table compares their roles; the reviewed guidance does not establish one vendor or product as best.
| Approach | Useful role | What it must still do |
|---|---|---|
| In-process policy middleware | Checks requests close to the application’s tool execution code. | Enforce identity, operation, resource, and parameter scope before side effects; preserve logs and appropriate failure behavior. |
| API or agent gateway | Centralizes authentication, authorization, rate limits, and interaction logging across calls that pass through it. | Evaluate targets and arguments, not only whether a tool name is allowed; apply action-bound approval and credential controls where needed. |
| Provider tool settings | Restricts the tools or tool-choice behavior exposed to the model. OpenAI documents the example modes and configuration in its Chat API reference. | Do not treat model selection restrictions as application authorization; check the identity, resource, and exact operation at execution time. |
When comparing implementations, assess whether enforcement is independent of model output; how precisely it scopes identity, operation, resource, and parameters; whether it supports action-bound approval and short-lived credentials; and how it handles egress, isolation, rate limits, auditing, and failure. Portability may also matter if policies need to apply across providers or execution paths.
What should you monitor and test after launch?
Permission boundaries need operational ownership. Keep auditable records of tool calls, parameter changes, authorization decisions, and approvals. Avoid logging raw credentials or unnecessary sensitive content. Monitor unusual call rates, unexpected tool chains, and changes in behavior, and set ceilings for calls, retries, tokens, and spend to contain runaway loops.
Test adversarial cases before production and after meaningful changes to tools or policy. Include prompt injection, attempts to escape resource scope, replayed approvals, ambiguous tool names, and policy-service failures. Verify not just that a request is denied, but that no side effect occurs when authorization or approval cannot be established. OWASP’s AI Agent Security Cheat Sheet, Securing Agentic Applications Guide 1.0, and Top 10 for Agentic Applications 2026 provide related security guidance.
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