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You cannot guarantee that an AI agent will never make a mistake, but you can limit the damage it can cause. Give it only the tools and data it needs, enforce permissions outside the model, and require meaningful approval for consequential actions. Treat anything it reads from outside your trusted system as potentially misleading or hostile.
1. Limit what the agent can access and do
Start with the agent’s authority, not with a longer instruction telling it to be careful. A prompt may guide the model, but it is not a dependable access-control boundary. Use the identity, tool wrapper, or policy layer that actually authorizes execution to restrict actions.
- Give the agent only the tools required for its assigned task. OWASP’s AI Agent Security Cheat Sheet recommends granting agents the minimum tools needed for a specific task.
- Limit the data and resources each tool can reach, not just the number of tools available. OpenAI advises: “Where possible, limit an agent’s access to only the data it needs to complete the task.” See Understanding prompt injections.
- Where the platform supports it, use a distinct agent identity and grant it only the roles and permissions necessary for the task. Google Cloud gives this least-privilege advice in its AI security and safety guidance.
- Remove tools the agent does not need, especially broad access to accounts, files, production systems, or tools that can make irreversible changes.
For example, an agent asked to summarize documents should not also have permission to send email, delete files, or update production records unless those actions are part of its actual job.
2. Enforce permissions before a tool runs
Have a trusted component check each proposed action before it executes. That component should validate the tool, its parameters, the target resource, the agent’s scope, and any required approval. OWASP describes this separation in its AI Agent Security Cheat Sheet: the agent can propose an action while a policy or execution component independently checks whether it is allowed.
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Do not treat the model’s own claim that an action is safe—or that a user authorized it—as authorization. The enforcement point must reject calls that exceed the agent’s permissions, even if the model requests them confidently.
3. Separate reading from changing things
If a task only requires analysis, use read-only access. Add write access only when the agent must make a change, and scope that access to the relevant resources. When an agent does need to modify something, distinguish low-impact, reversible edits from actions such as publishing, deleting, transferring, or changing production settings.
Approval controls depend on the platform. For example, OpenAI’s Evals API reference documents MCP tool approval settings, including filters based on read-only annotations and tool names. That is a platform-specific example, not a universal setting or default for every agent framework.
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4. Require review for consequential actions
Require a human or an independent policy check before actions that could cause substantial harm, expose sensitive data, or be difficult to undo. Examples include publishing externally, deleting or overwriting important information, changing access controls, and making consequential updates to production resources.
An approval request should show the proposed action clearly enough for a reviewer to evaluate it: what will change, which account or resource is affected, and what content or values will be sent or modified. A vague “Approve?” prompt is not meaningful oversight.
Human review lowers risk but does not eliminate it. Google Cloud warns that a reviewer may approve a destructive action without properly verifying that it is safe. Reviewers should inspect the actual action and its target rather than simply trusting the agent’s summary.
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5. Treat external content as untrusted
An agent may encounter instructions embedded in documents, web pages, emails, or other content it was asked to process. Prompt injection attempts to use that content to redirect the agent—for example, to reveal information or take an action outside the user’s request. OpenAI explains this risk in Understanding prompt injections.
Tell the agent how to treat outside content, but do not rely on that instruction alone. Restrict the data it can access, limit available tools, enforce permissions independently, and use isolation appropriate to the task. Google Cloud’s AI security and safety guidance also flags insecure tool chaining and naive error handling as risks. Anthropic’s Trustworthy agents in practice describes prompt-injection defense as a matter for protections at multiple levels, alongside human control, transparency, and privacy.
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The right setup depends on what the agent can reach and what its actions can change; there is no single configuration established for every task or framework. Use stronger controls as the potential impact rises. Consider these factors when reviewing an agent:
- Tool and data scope: Can it reach only task-relevant tools and resources?
- Independent enforcement: Does a trusted component validate the action before execution?
- Approval quality: Which actions require approval, and can the reviewer see the exact change?
- Untrusted inputs: Could content the agent reads contain hostile instructions, and are permissions and isolation still effective if it follows them?
- Impact and reversibility: Could an error expose data, disrupt a service, or make a change that is hard to undo?
OpenAI’s developer quickstart describes agents that use built-in and custom tools, including functions that call APIs or run code. This illustrates why controls must cover the tools an agent actually uses; the implementation details vary by platform.
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