The Tool Desk
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What is an AI agent?
Anthropic defines an agent as “an AI model that directs its own processes and tool use when accomplishing a task”—deciding how to achieve a user’s goal rather than following a fixed script. In practice, an agent can plan, take an action, observe the result, adjust its approach, and repeat until it finishes or needs human input. Anthropic’s explanation of trustworthy agents describes this self-directed loop.
That distinguishes an agent from a chatbot that simply answers a question. A chatbot may explain how to organize a trip; an agent might search for options, compare them, and prepare a booking for approval. The defining feature is not a conversational tone. It is the system’s ability to make choices about how to carry out a task and use tools along the way.
What determines what an agent can do?
An agent is more than its underlying AI model. Its behavior and reach also depend on the software around the model, the tools it can use, the permissions it receives, and the environment in which it operates. NIST describes contemporary agent systems as general-purpose models embedded in software scaffolding that enables tool use and actions beyond text generation. It treats tool autonomy—the freedom to take initiative or use tools without user intervention—and monitoring as important dimensions of an agent system. NIST’s overview of tool-use agent systems explains these distinctions.
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For example, an agent that can only draft an email has a different reach from one that can send it. An agent that can read a file does not necessarily have permission to edit or share it. Capability and permission are separate: available integrations and access controls determine which actions are possible, while the system’s autonomy affects how often it can take those actions without checking first.
Why can AI agents be hard to control?
They make a chain of decisions
A multi-step task gives an agent more opportunities to misunderstand what the user meant, make a mistake, or continue from an incorrect observation. If a user asks it to “handle” a problem without specifying boundaries, the agent may choose steps the user did not intend. Less frequent human oversight can leave more room for those choices to accumulate into unintended consequences, a concern Anthropic highlights in its discussion of agents.
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They may act on untrusted content
An agent that reads a webpage, email, or document can encounter instructions placed there by someone other than the user. Indirect prompt injection is an attempt to use that content to redirect the agent—for example, to disregard the task or take an action the user did not request. OpenAI describes this risk in its overview of prompt injections and in its discussion of ChatGPT agent.
The danger depends on what the agent can access and do. If it reads a hostile instruction but has no sensitive data or action permissions, the possible impact is limited. If it can also access private information or take direct actions, successful manipulation could have more serious consequences. This is a risk pathway, not proof that every agent is vulnerable in the same way or that every attempt succeeds.
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Small errors can have consequential effects
A mistaken answer is different from a mistaken action. An error in a draft can be corrected before anyone sees it; an error in a sent email, purchase, published post, or changed record may be harder to undo. The greater the agent’s access and authority, the more important it is to limit what it can do without review.
How can you assess an agent’s level of risk?
When evaluating an agent for personal use or deployment, look beyond its advertised capabilities. These questions help reveal how much independent control it has and what could happen if it goes wrong:
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- Autonomy: How many decisions can it make before it asks for direction or approval?
- Access: Which files, accounts, websites, APIs, or business systems can it read or change?
- Action impact: Can it only draft or recommend, or can it send, buy, publish, execute, or alter records?
- Observability: Can you see what actions it took, and are those actions logged?
- Human control: Can you pause it, take over, reject a proposed action, or require approval first?
Two systems described as “AI agents” may differ substantially across all five dimensions. NIST’s work on identity and authority for software agents highlights the security importance of access to data, tools, and applications, along with identification, authorization, and auditing.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What safeguards help keep an agent under control?
For people using an agent
- Give it only the access required for the specific task. A research request usually does not require connecting a sensitive account.
- State the task and its boundaries clearly. Specify what it may read, change, or prepare, and what it must not do.
- Require confirmation before consequential actions such as sending an email or completing a purchase. Review the details before approving.
- Use pause, takeover, or other available user controls when you need to inspect what it is doing.
OpenAI describes confirmation controls for consequential actions in its ChatGPT agent announcement, and discusses user intervention alongside prompt-injection risks in its prompt-injection guidance.
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For organizations and developers
- Limit permissions to the minimum needed, and make each agent’s identity and authority clear.
- Log and audit actions so teams can determine what happened and which systems or data were involved.
- Monitor behavior and provide a human escalation path for high-impact or unusual actions.
- Use layered protections, including security controls and testing, rather than relying on a single prompt filter to stop malicious instructions.
NIST’s guidance on agent identity and authorization points to identification, access control, and auditing as adoption concerns. OpenAI describes layered security, monitoring, and user-control measures in its prompt-injection guidance. These safeguards can reduce risk, but none guarantees that every error or attack will be prevented.
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