An AI agent is a software system that pursues a goal by choosing and taking actions—often through tools or connected services—and adjusting its next steps based on what happens. That ability to act can make an agent useful, but it also means it may misunderstand a request, make an error, or encounter hostile instructions in content it reads. “Agent” does not mean fully autonomous or free to act without approval: capabilities and oversight depend on how the system is built and configured.
What is an AI agent?
There is no single definition used everywhere. Definitions commonly emphasize three features: an objective, outputs that can affect what happens next, and some degree of autonomy. Other features—such as adapting to an environment—are not treated as essential in every definition. The OECD’s February 2026 review compares these competing concepts in The agentic AI landscape and its conceptual foundations, while NIST describes agentic AI in terms of goal-directed behavior, autonomous decisions, and interaction with users and systems in its Agentic AI overview.
A chatbot can answer a question with text. An agent can also choose steps and use tools to work toward a goal. For example, an assistant might explain how to arrange a meeting; an agent with calendar access might check availability, draft an invitation, and, if configured and allowed, send it. The word “agent” alone does not tell you which of those actions are enabled.
In its developer documentation, OpenAI describes an agent as a model paired with instructions and optional runtime components such as tools, guardrails, handoffs, and structured outputs. Anthropic’s definition likewise emphasizes a model directing its own processes and tool use rather than following a fixed script. The model is only one part of the system: integrations, permissions, and approval rules determine what it can attempt. See OpenAI’s agent definitions and Anthropic’s Trustworthy agents in practice.
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How does an AI agent work?
Many agents work in a loop: interpret the objective, decide on a next step, take an action, inspect the result, and decide whether to continue or ask for human input. The loop can involve one tool or several, and it may stop for approval rather than acting independently. Anthropic describes this pattern as planning, acting, observing, adjusting, and repeating until the task is complete or the system needs to check in.
- Interpret the goal. The agent uses the request and its instructions to determine what outcome to pursue.
- Choose a step. It selects an action or tool that appears useful, such as searching a website or checking a calendar.
- Act and observe. The tool returns information or changes something, and the agent uses the result to decide what to do next.
- Continue or hand off. It may repeat the cycle, present a result, or request human input, depending on its design and permissions.
The surrounding system determines the agent’s reach. OpenAI’s ChatGPT agent system card, for example, describes one product with multistep research, a remote visual browser, a terminal for code and data work, and connectors to external applications. Those are features of that product, not a checklist for every agent. Details are in the ChatGPT agent System Card.
Why might an AI agent take an action you didn’t expect?
The request leaves room for interpretation
Words such as “organize,” “handle,” or “clean up” do not specify every step or boundary. An agent asked to organize files might decide that moving folders or removing apparent duplicates is part of the task. That choice may seem like a reasonable way to pursue the stated goal while still being different from what the user intended.
It infers steps that were never explicitly requested
An agent is designed to select actions toward an objective, not merely repeat the user’s words. It can therefore take an inferred step that seems useful to the system but crosses a boundary the user had in mind. This is a mismatch between the goal as interpreted and the user’s actual intent, not evidence that the agent consciously chose to disobey. Anthropic discusses this problem in its framework for developing safe and trustworthy agents.
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A model or tool can make an ordinary mistake
Tool use adds opportunities for practical errors: the agent could mistype an email, select the wrong item, or take an action such as deleting a document. OpenAI lists examples like these in its Computer-Using Agent safety article. A correct plan does not guarantee that a tool will behave as intended or that the agent will interpret its result correctly.
Permissions turn a decision into a real-world consequence
There is a meaningful difference between an agent that can read information and one that can write to an account, send a message, make a purchase, delete a file, or publish content. NIST’s August 2025 workshop summary recommends assessing tool use through dimensions such as access patterns, action criticality, reversibility, reliability, monitoring, and autonomy—not by relying on a single risk score. Read Lessons Learned from the Consortium: Tool Use in Agent Systems.
Content it reads can contain hostile instructions
Prompt injection is an attempt to use instructions embedded in content—such as a web page or document—to steer a model away from its intended instructions. It is an active security challenge, not proof that every agent will be compromised. The risk matters especially when an agent can both consume untrusted content and take consequential actions. Anthropic and OpenAI describe the issue and related safeguards in their respective agent framework and computer-using agent safety article.
Information may carry across contexts
Some systems retain information across tasks. If sensitive details are carried into a different context where they do not belong, that can create a privacy problem. Anthropic discusses this risk alongside other agent safety considerations in its framework for developing safe and trustworthy agents.
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How can you assess an agent’s risk?
Do not judge an agent by its label. Look at the specific deployment: what it can access, what actions it can take, how much initiative it has, and how visible those actions are. A read-only research helper and an agent that can send email or alter records may use similar models yet pose very different practical risks.
- Tools and reach: Which sites, files, accounts, services, or physical controls can it access?
- Permission level: Can it only read, or can it write, send, buy, delete, or publish?
- Autonomy: Which steps can it take on its own, and when does it ask for input?
- Impact and reversibility: How serious would a mistake be, and can the action be undone?
- Observability and oversight: Can you inspect its activity, preview results, monitor it, or approve an action before it happens?
- Reliability for the task: What could go wrong in the model’s decision or in the connected tool?
These dimensions reflect NIST’s guidance for thinking about agent tool use. They help identify what deserves tighter controls without pretending that one universal rating captures every deployment.
What safeguards can reduce unwanted actions?
For personal use
- Give the agent the smallest set of permissions needed for the task.
- Review drafts and proposed changes before allowing them to be sent, published, or applied.
- Where the product permits it, require confirmation for consequential or hard-to-reverse actions.
- Pay attention to which connected accounts and tools are enabled, especially when the agent reads untrusted content.
Controls vary by product. OpenAI says its Operator account can require confirmation before actions such as submitting an order or sending an email, and can require active supervision on some sensitive sites. Anthropic describes MCP controls that can allow or prevent access to particular tools, with one-time or permanent access choices. These are vendor-described examples, not guarantees shared by all agents; see OpenAI’s Computer-Using Agent and Anthropic’s agent framework.
For organizations
Govern the actual deployment rather than treating “AI agent” as one risk category. Map the tools and permissions it uses, assess reliability and potential harms, decide which actions need approval, and make activity observable. NIST presents these as complementary considerations in its tool-use workshop summary.
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